Craniometrics, phylogeny, and race

Craniometry is once again being used as a tool for understand population relationships. OK, it was never abandoned, but it’s undergone something of a resurgence.

But, let’s begin with Franz Boas: a major critic of typological racial thinking, he and his followers were very influential in introducing environmental factors into anthropometry – stressing the importance of health, diet, etc, on patterns in the skeletal form. In a famous study of European immigrants and their children who were raised in the USA, he found the children had different cranial measures than the parents. Though Boas wouldn’t have argued that  there was no biological component to human variation, it marked a trend towards focussing on anthropometrics as measuring environmental changes, and a rejection of racial classifications.

But a few years ago, there was some dispute over whether Boaz’ conclusions really held up on re-analysis of his work. Relethford, in “Boas and Beyond: Migration and Craniometric Variation” (2004), responding to both sides, does a good job of getting to what is important: there was indeed a change in cranial measurements – but that did not obscure an underlying pattern, which you can see in this graph:

It turns out that we can get multiple pieces of data from anthropometric data:

– environmental (health/diet/etc)
– phylogenetic
– adaptative differences

The three all run the risk of obscuring each other, but we have reason to believe that they have not.

Population level differences can be caused by: health, diet, physical activity, etc. but, as shown above, they do not eliminate phylogenetic data. Neither does adaptation. If all populations were adapted for their own environment, then their phenotypic characteristics should be similar only in so far as their habitat is similar. But here we see another table from Relethford, comparing phenotypic difference to geographic distance, which shows that, similarly to the neutral genetic data, phenotypic distance increases with geographics distance, indicating that, like with our genetic data, our phylogenetic information remain intact because much of our phenotypic variation is neutral.

There has, of course, been adaptation as well, it’s just that it hasn’t obscured the neutral cranial variation. In the next table you can see the Fst values for a number of cranial measurements. Fst values measure population differentiation, and thus higher Fst values are likely to indicate selection (those Fst values that are above 0.3 are bolded and may likely indicate selective pressure).

As mentioned before, this is all very convenient: we can look for adaptive changes through high Fst values, but there clearly hasn’t been so much that phylogenetic information has been obscured; and we also can look at anthropometric data for all kinds of information on past health, but neither does this totally obscure phylogeny, which allows us to look at population relationships.

However, the craniometric data is not the same as neutral genetic data – it has less population structure, and is “less able to identify nonclinal variations among populations (which would be in accordance with the existence of biological races in the human species) than molecular data are” (Strauss & Hubbe, 2010).

Strauss & Hubbe use a dissimiliarty fraction (ω): ” the proportion of pairs of individuals from the same population that is genetically more different than pairs sampled from different populations.” In the context of genetic data, using the dissimilarity fraction showed that, once enough loci were sampled, the notion that people from the same population are often more genetically distinct than those from different populations, was falsified – reaching 0 after 800 loci were studied – in other words: “when more than 800 loci are considered, no pair of individuals from the same population is more different than any pair of individuals from any two populations.”

From genetic data, ω eventually reaches 0 with enough loci

However, when they used this method on craniometric data, the same result was not found. When using genetic data, ω decline to 0; however, with craniometric data ω reached only a mean of 0.3. That is, about a third of the pairs within a population are more different than pairs between populations. This is despite the samples being obtained from widely separate populations, which should have enhanced differentiation.

ω for cranial measurements never has better resolution than the equivalent of 20 loci in the genetic system

A helpful note on the lack of resolution of craniometric data is provided by the authors when they note that “the population history signal of human craniometric traits presents the same resolution as a neutral genetic system dependent on no more than 20 loci.”

As mentioned, this means craniometric data supports “the notion of an absence of discrete biological groups… [and] indicates that cranial morphology is less able to identify nonclinal variations among populations.”

But then, why the success in using craniometric data to sort skulls correctly into populations? Strauss and Hubbe point out the importance of ‘centroids’ in these studies: “…classificatory analyses achieve high levels of success because they depend on the a priori definition of group centroids.As a consequence, when a large number of variables is considered, the probability that this kind of analysis will find a dimension in the original data that differentiates among the a priori defined groups is high. Yet the precise biological significance of this kind of difference is hard to establish, especially when the high values of dissimilarity fractions reported here are considered. High rates of correct discrimination of groups can thus be misleading in understanding the structure of human biological diversity.”

3D measurements

Linear measures

However, this is not, I think, the final word on craniometry and population structure. It is possible that newer craniometric techniques are changing the picture. For example, traditionally measurements have been linear, but new 3D methods are being developed that show greater ability to sort into populations.

Consider for example a recent paper: “Identification of Group Affinity from Cross-sectional Contours of the Human Midfacial Skeleton Using Digital Morphometrics and 3D Laser Scanning Technology” (2011).

All samples (90) plotted along the axes of the discriminant functions based on 13 select Fourier coefficients.

The authors compared their ability to sort into populations, using 3D measuring of the midfacial region with traditional linear measurements of the same region. They found they were able to sort into their 3 test populations (European, Chinese & Native Californian) based on only the midfacial region with an average accuracy of 86% using 3D measurements compared with 57% using the linear measurements. It may be that the greater sensitivity of these new techniques may change the picture painted above in terms of population structure resolution achievable with craniometric data (Strauss and Hubbe, in calculating the dissimilarity fraction, used the 55 linear measurements of the skull from Howell’s (1995) database). Still, as they note: “the subasymptotic behavior of the ω curves indicates that craniometric measurements become highly redundant when more than 30 variables are included in the analyses.” However, the newer 3D measures have found some contours to be “rich in diagnostic shape information.”  I would also be interested in seeing what the results of using all anthropometric (not just cranial) measurements, would be.

The fairer sex

Literally. The data seems to suggest that worldwide, women are fairer skinned than men, and also have fairer hair. However, among Europeans, eye colour does not follow a similar pattern. Instead, men seem to be more likely to have blue eyes (though women are more likely to have green eyes).

Here are some tables of data on sex differences in hair and eye colours.

First, from the National Longitudinal Survey of Youth, via GNXP. As you can see, blue eyes are more common among men than women, whereas green eyes are more common in women. But women are more likely to have fair hair.

From another study we have data from three northern European nations (again, via GNXP). This one shows also that men are more likely to have blue eyes, and women to have green eyes. This study also shows that women have fairer skin (skin sensitivity being an indicator). Not much of a difference in hair colour though.

Data from the US National Health and Nutrition Examination Surveys, also shows a male-female gap in blue eyes. From “Cohort effects in a genetically determined trait: eye colour among US whites” (2002).

There is more data from Finland, which finds a similar pattern of blue-er eyes among men, but little evidence of women having lighter hair.

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Update: from Gender is a major factor explaining discrepancies in eye colour prediction based on HERC2/OCA2 genotype and the IrisPlex model (2013), is new data and a collection of data on blue eye frequency among men and women in various European countries:

sex-difference-in-light-eyes

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Peter Frost has written a fair bit on this topic, arguing that in Europe, until “10,000 years ago, Europe had vast expanses of continental tundra—an environment where male hunters provided almost all the food and where long-distance hunting caused more deaths among young men than among young women” – and thus, there was stronger sexual selection on women, leading to the exaggeration of feminine traits (whereas he argues the opposite for Africa – that in Sub-Saharan Africa women have been able to largely feed themselves and their offspring, has resulted in stronger sexual selection on men).

He has consequently argued that these European traits represent something of a feminization and are sex-linked. The blue-eye fact, is, of course, somewhat contradictory to this hypothesis, in that men are more likely to have blue eyes (though women are more likely to be green eyed). The fair hair is interesting, as it is more common in juveniles and tends to darken as people age, and girls, who actually start off, on average, with darker hair than boys, end up with lighter hair, on average, in adulthood. The association of lighter hair with children and women is also found in other groups, such as Australian Aboriginals and Papua New Guineans. A 2008 study (“Spectrophotometric Methods for Quantifying Pigmentation in Human Hair—Influence of MC1R Genotype and Environment”) using twins, also found that females had lighter hair than their male twins, as well as greater variability. Frost considers female colour variability to be the key, in hair colour and eye colour.

It has been argued by Cochrane and Harpending (2010), and Eiberg (2008), that the origin of blue eyes dates from 6-10,000 years ago, which would seem to be too recent for the spread to be explained by Frost’s thesis. However, Frost disputes these dates as not based being based on any real analysis.

[See also my post on the distribution of fair hair and eyes around Europe].

Distribution of light hair and eyes in Europe

The first two maps have been taken from eupedia, which has colourized the maps contained in a paper by Peter Frost (2006), which in Frost’s words, were “reproduced from an anthropology textbook (Beals & Hoijer 1965, pp. 213-214). Beals and Hoijer, in turn, cite a textbook by another anthropologist, Frederick Hulse (1963: p. 328). Unfortunately, Hulse does not indicate the provenance of his data. I suspect he was using data from military recruits, with a lot of interpolation. Or perhaps he was using even earlier maps.”

Next, we have a map from Carleton Coon (‘The Races of Europe’), and attributed to ‘Elmer Rising’ (1939):

This map produced by Bertil Lundman (1965):

Here is a map created with 23andme data on a simple scale of more blue eyes to more brown:

From the paper “DNA-based eye colour prediction across Europe with the IrisPlex system.” These samples are drawn from specific regions, so they don’t necessarily represent the whole country. For example, the northern Italy sample has higher proportions of blue eyes than it would if it included southern Italy. The sample from Northern Ireland has higher proportion of blue eyes than I would expect based on other measurements of Ireland and Britain.

Finally, some extra data (locally collected, not national):

Two studies of different schools in Sweden found 79% and 79.6% blue eyes (“Frequency and Distribution Pattern of Melanocytic Naevi inSwedish 8–9-year-old Children” & “Prevalence of common and dysplastic naevi in a Swedish population”)

And another of a similar type in Estonia found 82% in Estonia (“Frequency and distribution pattern of melanocytic naevi in Estonian children and the influence of atopic dermatitis”)

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Compare the above maps to the following genetic map of the averaged frequencies of 3 genes that are involved in light hair and eyes:

Update:

From a 2013 Slovenian study with 105 subjects (Prediction of eye color in the Slovenian population using the IrisPlex SNPs), 44.7% had blue eyes, 29.6% brown eyes, and 25.7% intermediate colours.

A world map of predicted eye colour based on genetic variants, from IrisPlex: A sensitive DNA tool for accurate prediction of blue and brown eye colour in the absence of ancestry information (2011)

2011 world eye map predictor colour

A world map of predicted hair colour based on genetic variants, from The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA (2013)

2013 prediction of hair colour

Distribution of eye colours, based on phenotypes (pie chart shows size of sample), from Further development of forensic eye color predictive tests (2013)

ruiz et al 2013 - actual phenotype - pie chart proportional to size of sample

Phenotypes of sample in Spain, from Gender is a major factor explaining discrepancies in eye colour prediction based on HERC2/OCA2 genotype and the IrisPlex model (2013). Striped grey is blue eyes, light grey is intermediate, and dark grey is brown-eyes.

2013 spain map

Update II:

Data from the Blue Eyes Project run by ScotslandsDNA, map found on the Daily Mail:

article-2738224-20ECE9AF00000578-656_634x632

 

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Of course, light hair and eyes exist well outside of Europe as well.

See also my post on how the distribution of eye and hair colour is not evenly distributed between men and women.

Pygmies, life-history, and maturation

After writing in an earlier post about Rushton’s & Lynn’s theories of black precociousness, it occurred to me to write about Pygmies’ unusual maturation pattern.

Unlike many other groups for whom there is a suspicion of different genetic potentials for height, but no absolute evidence, for Pygmy’s the matter has been pretty confidently settled. Here’s a map of pygmy world distribution. The black circles are groups that are not technically pygmy’s, but are also fairly short (notice their close geographical proximity).

From: "Evolution of the human pygmy phenotype" (2008)

[Note: there is conflicting evidence on whether pygmy and nearby shorter peoples cranial capacity is lower than surrounding African groups (1,2)]

The evidence for a genetic cause to the pygmy height:

“…three lines of evidence suggest that this phenotype is determined principally by genetic, rather than environmental, factors. First, … genetic disruptions of the growth hormone (GH) and insulin-like growth factor I (IGF1) pathway are likely to have etiological roles … Second, the childhood growth rates of some rainforest hunter-gatherer populations are surprisingly fast… for at least some populations, small adult body sizes are a reflection of relatively slow growth in adolescence rather than childhood… which is inconsistent with a simple model of stunted growth from poor nutrition. Indeed, other populations that also endure frequent episodes of nutritional stress still achieve adult heights that are greater than those of rainforest hunter-gatherers… Third, the offspring of Efe mothers and Lese (agriculturalist) fathers have statures intermediate to those of the two parental populations…”
– “Evolution of the human pygmy phenotype” (2008)

[Update: a new study looking at the genomes of Pygmies and their Bantu neighbours found that, among Pygmy, having more Bantu ancestry was correlated to being taller.]

It’s also worth noting that the geographical distribution of this phenotype, across genetically diverse populations, makes it highly unlikely that genetic drift could be a factor. So then what drives the pygmy phenotype.

Various explanations have been provided: thermoregulatory, improved mobility (reduced metabolic costs), dietary limitations, and most recently, a life-history proposal of high death rates in young-adulthood (source).

Correlation between adult height and mortality in short-populations. (Evolution of the human pygmy phenotype, 2008)

The idea lying behind the life history account is that the high death rate amongst the young promotes early truncation of growth to permit earlier reproduction. A recent archaeological study of a Khoisan (not pygmy, but also small) skeletal record, however, does not support the thesis of increased juvenile mortality (source). However, they also did not find an early cessation of growth, which the earlier (Migliano, 2007) study did find, among actual pygmies. Likewise, the Migliano study did find a higher juvenile (and adult) death rate among the (contemporary) pygmy’s. So while the Khoisan study contradicts what one would expect, there is clearly differences between the two populations studied. Not surprising since they inhabit pretty different environments as well.

[In any case, fortunately for the Pygmy’s, height preferences in a mate may not be universal.]

Population differentiation in brain/skull structure

Whatever the significance of it, there is certainly evidence that our brains have not stood still since we left Africa. All populations have changed due to selection and drift. There are three lines of evidence we will look at:

1) Past and present variation in cranial capacity
2) Genetics
3) Structural brain variation

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Variation in Cranial Capacity
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Beals et al. (1984). Variation in cranial capacity.

Despite the idea being (baselessly) ridiculed by Stephen J. Gould(1), it has been found that cranial capacity varies among populations in a generally latitudinal cline, with southerly populations having smaller skulls, and increasing in size as one moves northwards. (2,3) This variation has existed for thousands of years(4).

There has also been a worldwide decline in cranial capacity, which happened at different rates and to different degrees in different populations, over the last 10-20,000 years – but in general cranial capacity shrunk by over 100cm3. (4)

Craniometric studies on modern populations have also found differentiation on a number of cranial measures, much of which is neutral, but some having high fst values (such as cranial breadth), indicating selection(5).

In sum, not only did the average cranial capacity of populations diverge as they migrated throughout the world, but they later began, independently, to shrink. We also know that the differentiation in cranial capacity is likely due to selection, as was the global trend towards smaller brains. It is clear that our brains were being remodeled well after the Out of Africa event and the dispersal of populations.

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Genetic evidence
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Genomic studies on modern populations have found not just population differences in genes related to neuron development, but strong evidence of selection.

Hu & Zhang (2011)

Wu and Zhang (2011) found high levels of population differentiation in genes involved in the nervous system. See the figure to the right – specifically, genes related to neuron development, positive regulation of neuron differentiation, hindbrain development and dorsotubal neural tube patterning all have as much, or much higher levels of population differentiation, than pigmentation. This strongly indicates selection on the nervous systems of various populations.

Also: Pickrell, Coop, Novembre, et. al., 2009. Signals of recent positive selection in a worldwide sample of Human Populations:

“The NRG–ERBB4 signaling pathway is well-studied and known to be involved in the development of a number of tissues, including heart, neural, and mammary tissue (Gassmann et al. 1995; Tidcombe et al. 2003). Variants in genes in this pathway have been associated with risk of schizophrenia and various psychiatric phenotypes (Stefansson et al. 2002; Hall et al. 2006; Mei and Xiong 2008). We suggest that an unidentified phenotype affected by this pathway has experienced strong recent selection in non-African populations”

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Structural Brain Variation
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There is some suggestive evidence of structural differences in the brain between populations. However, this is a trickier area, and may well be caused by environmental/cultural factors. Klekamp et al (1994) reported that Australian Aborigines have significantly larger visual cortices than Europeans. Differences have been found by Chee et al (2011) in a comparison of Chinese and Americans, and, in another study, between white and black Americans (source). It is however, known that brain structure can respond to environmental demands (source).

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See Race, genes, and disparity for an in depth look at the issue of brain size and IQ

Rushton, Lynn, and black precociousness

[update: see also my post on race and sexual behaviour]

Rushton, in Race, Evolution, and Behaviour (1995), makes various claims about the differing rates of maturation between races. I want to get an idea of the current state of these claims. The areas in which Rushton claims different rates can be grouped into the following:

– Gestation period
– Psychophysical development
– Ossification (incl. dental development)
– Puberty
– Sexual Debut

Gestation period

The black/white difference in gestation period has been confirmed in various studies in the U.S. (sources: 1, 2) Britain (sources: 1, 2), and African countries (sources: 1, 2). Recent studies have found it plausible that at least part of the cause is genetic (source), and researchers have begun looking for the specific genes involved (source). Although environmental explanations are still being sought (source). Additionally, there is evidence that, despite black infants being born ‘prematurely’, they have experienced accelerated maturity:

“The suggestion that maturity occurs earlier in gestation in Black babies is consistent with many reports over the last 20 years that black babies suffer less from respiratory distress syndrome than white European babies for any given gestation at birth… Consistent with the accelerated pulmonary maturity seen in preterm black babies, neonatal survival in black babies is also higher than that of white Europeans.”

(source)

Pyschophysical development

Rushton cited studies showing faster black infant development on various indexes, including sitting up, walking, turning over, etc. We should also discuss Richard Lynn’s formulation of ‘black infant precocity’ (source) – which essentially posits that black infants are more advanced psychophysically until around 15 months, after which point the lead disappears. A number of studies over the last decades have found a pattern of black infant precocity in psychophysical development. A 2007 study from South Africa entertains Lynn’s idea:

“Another possible explanation for the findings of the current study is the concept of Black infant precocity (Lynn 1997). Lynn compared Black South African infants’ performance on the Bayley Scales of Infant Development to the American standardisation sample and found that the Black infants were significantly more advanced in terms of their mental and motor development during approximately the first 15 months of life. This age range is similar to that of the sample in the present study (i.e. 13 to 16 months). Positive evidence of this was first advanced by Falade (1955) who found that Black Senegalese infants assessed on the Gesell Developmental Screening Inventory were significantly more advanced in areas of fine-motor development, eye-hand coordination, problem-solving and object permanence than matched White American infants. Similar results were obtained with Ugandan infants (Gerber 1958), Nigerian infants (Freedman 1974) and Black South African infants (Richter-Strydom and Griesel 1984, Lynn 1997).”
(source)

Nevertheless, genetic explanations have not swept the field; from a 2011 paper:

“This “precocity” was initially interpreted as a biological, genetically driven phenomenon. Subsequent investigation led in other directions (Kilbride & Kilbride, 1975; Leiderman, Babu, Kagia, Kraemer, & Leiderman, 1973; Super, 1976, 1981; Varkevisser, 1973). First, it was demonstrated that traditional methods of infant care common in sub-Saharan Africa include deliberate teaching and practice of sitting and walking (and, sometimes, crawling). These customary practices, carried out by parents, siblings, and other relatives, reflect a local understanding of what young children are capable of, and this understanding is manifest from the infant’s earliest days (Super & Harkness, 2009). Further, careful observation revealed high levels of leg, trunk, and back exercise, and also vestibular stimulation, incidental to customary methods of holding and carrying the infant… When families migrate from traditional rural areas to an urban environment such as Nairobi, they adapt to quite different physical and social settings, and they come in contact with a greater variety of ethnotheories: Both daily life and infant motor development shift toward the Euro-American pattern. (source)

(Chapter 4 of Handbook of cultural developmental science also offers an in-depth cultural explanation to differing rates of motor development). Certainly culture has an important role to play in the variation of psychophysical maturation, but one can also point to the fact that African Americans likewise consistently show motor precocity (sources: 1, 2, 3, 4, 5), as do Caribbeans (source). And one might also wonder why Africans show such consistent ‘local’ understandings (see references to Africa wide findings above).

Some studies have shown differences between whites and asians as well. There is a good discussion of past results in Motor Development  in Canadian Infants of Asian and European Ethnic Origins (2009), which itself found no difference between asians and whites in Canada. Studies of asians have either shown no difference with whites, or slower development than whites, whereas studies comparing blacks have all (as far as I’m aware) found faster psychopysical development.

It is suggestive that the results have been broadly consistent with Rushton’s pattern of blacks > whites > asians. However, the varying results of asians vs. whites suggests environmental factors (at least for those races). The asian case especially implicates culture, because it tends to be in the studies in which asians and caucasians live in the same country that they become more, or entirely, similar (source).

Connolly et al. (2011) in “The Influence of Ethnicity on Infant Gross Motor Development: A Critical Review” analyze past studies and find that “In the articles reviewed, the differences in motor milestone attainment between Black infants and Western norms averaged 1 month… As well, in ethnic groups that were delayed, the amount varied from 1-1.5 months compared to Western norms… Currently, we can only say that motoric differences exist between ethnicities but we cannot… explain accurately why and how these differences exist.”

Ossification rates (including dental development)

Earlier studies showed accelerated rates of ossification in early childhood for Africans, followed by retardation at later ages, which fits in with the ‘Black Infant Precocity’ theory. From Effects of ethnicity on skeletal maturation: consequences for forensic age estimations (2000):

“Hand skeleton development of a population of West African children aged between 10 days and 15 years was investigated by Massé and Hunt [39]. In comparison to children studied by Greulich and Pyle, they found early maturity in the early postnatal months, followed by deceleration and sometimes retardation in middle and advanced childhood. Marshall et al. [37] and Garn et al. [22] also reported comparatively accelerated skeletal development in Africans during their early years of age. On the other hand, there have been several studies which reported that in advanced childhood and adolescence no time difference in skeletal maturation existed between Whites and Blacks… Studies so far evaluated seem to suggest that there is a genetically determined potential of skeletal maturation which does not depend on ethnicity and is available for exploitation under optimum environmental conditions (i.e. high socio-economic status), whereas a less favourable environment may lead to retardation of skeletal maturation.”

This, however, is not really contradictory to the black precociousness position, which generally argues a faster development in earlier childhood, and prenatally, not a continuously accelerated pace, while the counter-evidence comes from later childhood. Rushton has, however, also generally ascribed precocity for blacks to a longer time period, not just in infancy (ie. through puberty). The early infancy data, sparse as it is, indicates possible black infant precociousness.  Unfortunately, I don’t know of any newer studies on early racial differences in ossification rates. However, the above cited evidence on black infant precocity in fetal development and motor development, provides at least a background which is generally supportive of what limited data is available.

The third molar shows evidence of signficant differences in age of mineralization among races (source), black development stages being earlier than other groups, in the order black > white > asian.

Puberty

The U.S. data generally shows earlier age of puberty for blacks (source), but given how sensitive puberty is to environmental factors, and the lack of a global pattern, genetic explanations seem premature. Menarchy, for example, varies greatly among blacks depending on country, as well as other environmental factors such as urban vs. rural, SES, etc. Age of menarchy differs dramatically in general over time, and, in fact, between European countries as well..

Source: Determinants of menarche (2010)

Source: Menarcheal age among urban Kenyan primary school girls (2011)

Source: Menarcheal age among urban Kenyan primary school girls (2011)

Nevertheless, persistent racial differences in the U.S. lead some to consider genetic explanations:

“…controlling for height and either BMI or weight, the rate of early menarche remained significantly higher among black girls, suggesting that race is an independent factor of pubertal/menarcheal timing. The racial difference in pubertal maturation may reflect genetic factors. Black girls present higher insulin response to a glucose challenge, and subsequently increased levels of free IGF1, which is associated with skeletal and sexual maturation compared to white girls [36].” (source)

Sexual Debut

Once again, the U.S. data shows earlier black sexual debut (and later for asian) – (sources: 1, 2). However, blacks in America seem to begin sexual activity at a younger age than blacks in Africa (sources: 1, 23). Thus, data from the U.S. alone is likely misleading For example, in data from the U.S. we see “the median reported age of first sexual intercourse was 15.0 years for Black males, 16.3 years for Black females, 16.6 years for white males and females, 16.5 years for Hispanic males, 17.3 years for Hispanic females, and 18.1 years for Asian American males.” However, Sexual behaviour in context: a global perspective (2006) has global data on median age of first sex for men and women – and when I average out the median age of first sex for Sub-Saharan countries, I get 18.5 years for men, and 16.7 years for women (average 17.6 years – which is pretty close to what we get from the average of 7 western nations: 17.75).

Britain provides a good example of the variability because there are large numbers of both black Africans and black Caribbeans. A 2005 study found that, while white men began sexual activity at a median age of 17, for black Caribbeans the age was 15; however, black Africans began, like whites, at 17. (Indians and Pakistanis at 20). For women, whites and black Carribeans began at 17, while black Africans at 18 (Pakistanis: 20, Indians: 21). Similarly, a much larger percentage of black Caribbean males claimed to begun to have sex before age 16, whereas the number were about the same for white and black African males. For women, about the same percentage of white and black Caribbean women began having sex before 16, whereas significantly less black African women did. It should be noted that black African immigrants are often an elite sampling of their home country. It is of interest as well that Caribbeans have such an early sexual debut, similarly to American blacks – interesting because the global data referenced before does not show the Caribbean to have lower age of sexual debut. So what’s going on?

This last topic especially I’m going have to revisit in the future. Clearly, very little is settled in any of them.