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Briped2n1 This is the book of the generations of Adam. In the day that God created man, in the likeness of God made he him;

2 male and female created he them; and blessed them, and called their name Adam, in the day when they were created.

3 And Adam lived a hundred and thirty years, and begat a son in his own likeness, after his image; and called his name Seth:

4 and the days of Adam after he had begotten Seth were eight hundred years: and he begat sons and daughters:


Over at National Geographic Virginia Hughes has a very interesting follow up to her feature in Matter, Uprooted. It told the story of a woman who finds out that the man who she thought was her biological father was not, an her attempt using genetic genealogy to attempt to find blood kin. The ultimate ending was bittersweet, as the protagonist found a friend, but not a sister. But spoiler alert, it turns out that she did in fact find out who her father was! Nevertheless, not everyone was appreciative of the ending. Here is the first comment:

I don’t really understand why people do these things. This story worked out well enough, but it could have worked out very badly indeed, given the superstitious excitement some people have about ‘blood’. If someone was not part of one’s life in the world, even by report, then it seems to me they’re totally irrelevant.

This is a common sentiment. But the reality is it doesn’t really reflect much of our experience in revealed preferences. It’s common for many people, especially when they are young, to assert that there are so many children that need families that they’ll adopt. If I check on Facebook all the people who asserted this it turns out most of them ended up having biological children. There are practical reasons one can make for this in terms of one’s own life. Many traits are highly heritable, such as intelligence and personality, and children who are somewhat more like are easier to relate to. But this is really rationalization. Having biological children is a deeply human thing, selected for by evolutionary processes as a basic tautology. Those who lack this impulse do not flourish over the generations.

The whole reflex to dismiss biological ties as ‘superstition’ reminds me of something I saw on Facebook several years ago. A medical doctor of my acquaintance posted about “National Infertility Awareness Week”, and one of his “friends” decided to comment that he didn’t feel infertility was something to be sad about, seeing as anyone could adopt. This is again not a line of discussion that’s going to lead to reasoned argument. Obviously as a family we haven’t had to face infertility, but when you have children at an older age it’s someone you do think about it, and you are much more aware of the trauma and strain it causes in those who have experienced it. To just tell these people to adopt may seem “rational,” but actually it’s callous.

Ultimately it comes down to the facile assumption by some that they can reduce what the “Good Life” is to a few spare axioms and then infer for the rest of the human race what they should want. My post Against Vulgar Mohism for Our Age argues that attempts to reduce these sorts of highly textured and complex life decisions to rational elements of manipulable utilitarian algebras is futile and inhumane. Sometimes it is just best to smile and be happy for someone when they reach the end of a long hard road toward fulfillment, even if it isn’t your particular cup of tea.

• Category: Science • Tags: Genealogy 
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With DNA Testing, Suddenly They Are Family:

Several companies provide tests that can confirm whether adoptees are related to individuals they already know. Others cast a wider net by plugging DNA results into databases that contain tens of thousands of genetic samples, provided mostly by people searching for their ancestral roots. The tests detect genetic markers that reveal whether people share a common ancestor or relative.

Some experts on adoption and genetics have criticized ancestry and genealogy testing companies, saying they are, at times, connecting people whose genetic links are tenuous — in effect stretching the definition of a relative. Nevertheless, the growing popularity of the tests, combined with social media sites that connect people day to day, has given some adoptees a sense of family that feels tangible, intimate and immediate.


I think that these tenuous connections and slivers of information are better than nothing. This isn’t rocket science. And naturally many adopted people also could care less. This is a deeply personal issue, and the valence is going to be private. I suspect that those of us who aren’t adopted, and take for granted knowledge of our own family background have a hard time imagining the value which even a 3rd or 4th cousin could give someone.

Additionally, though finding very close relatives is not that common (first cousins, let alone first order relatives), knowledge of more distant relations can still help you triangulate aspects of family history if you begin with nothing. To give a personal example I know someone whose paternal grandparents were immigrants from Germany. The maternal side is much more mixed, and some of the genealogical records hit dead-ends in the mid 19th century in the USA. It turns out that one of the individuals that this person is closest to on 23andMe is an African American (both maternal and paternal lineages are clearly African). What does this mean? The lead hasn’t been followed up, but combining family histories might be very informative in this case.

• Category: Science • Tags: Genealogy, Genetics, Genomics, Personal Genomics 
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Image Credit: Anirudh Koul

One of the great things about the mass personal genomic revolution is that it allows people to have direct access to their own information. This is important for the more than 90% of the human population which has sketchy genealogical records. But even with genealogical records there are often omissions and biases in transmission of information. This is one reason that HAP, Dodecad, and Eurogenes BGA are so interesting: they combine what people already know with scientific genealogy. This intersection can often be very inferentially fruitful.

But what about if you had a whole population with rich robust conventional genealogical records? Combined with the power of the new genomics you could really crank up the level of insight. Where to find these records? A reason that Jewish genetics is so useful and interesting is that there is often a relative dearth of records when it comes to the lineages of American Ashkenazi Jews. Many American Jews even today are often sketchy about the region of the “Old Country” from which their forebears arrived. Jews have been interesting from a genetic perspective because of the relative excess of ethnically distinctive Mendelian disorders within their population. There happens to be another group in North America with the same characteristic: the French Canadians. And importantly, in the French Canadian population you do have copious genealogical records. The origins of this group lay in the 17th and 18th century, and the Roman Catholic Church has often been a punctilious institution when it comes to preserving events under its purview such as baptisms and marriages. The genealogical archives are so robust that last fall a research group input centuries of ancestry for ~2,000 French Canadians, and used it to infer patterns of genetic relationships as a function of geography, as well as long term contribution by provenance. Admixed ancestry and stratification of Quebec regional populations:

Population stratification results from unequal, nonrandom genetic contribution of ancestors and should be reflected in the underlying genealogies. In Quebec, the distribution of Mendelian diseases points to local founder effects suggesting stratification of the contemporary French Canadian gene pool. Here we characterize the population structure through the analysis of the genetic contribution of 7,798 immigrant founders identified in the genealogies of 2,221 subjects partitioned in eight regions. In all but one region, about 90% of gene pools were contributed by early French founders. In the eastern region where this contribution was 76%, we observed higher contributions of Acadians, British and American Loyalists. To detect population stratification from genealogical data, we propose an approach based on principal component analysis (PCA) of immigrant founders’ genetic contributions. This analysis was compared with a multidimensional scaling of pairwise kinship coefficients. Both methods showed evidence of a distinct identity of the northeastern and eastern regions and stratification of the regional populations correlated with geographical location along the St-Lawrence River. In addition, we observed a West-East decreasing gradient of diversity. Analysis of PC-correlated founders illustrates the differential impact of early versus latter founders consistent with specific regional genetic patterns. These results highlight the importance of considering the geographic origin of samples in the design of genetic epidemiology studies conducted in Quebec. Moreover, our results demonstrate that the study of deep ascending genealogies can accurately reveal population structure.

That paper found that nearly 70% of the immigrant founding stock in this data set came directly from France. For the period before 1700 that fraction exceeds 95%. Of the remainder, about 15% of the founding stock were Acadians, who themselves were presumably mostly of French origin. Because of the earlier migration of the French founding stock, they left a stronger impact on future generations:

Much of the difference here is because earlier ancestors in a population which went through demographic expansion would have more of an impact on the nature of the population than later contributors (the earlier ancestors would show up in many more downstream genealogies). But notice that the Amerindians in the pool are a much larger proportion of ancestors than their final genetic contribution (50% of the French Canadians had at least once Amerindian ancestor). I suspect this may be due to differential fertility because of variation in social status by race (i.e., mixed-race French Canadians having lower fertility, perhaps by way of their exclusion from highly fecund elite families), and not just later absorption of Amerindians than French (on the contrary, I suspect that Amerindians were assimilated earlier, not later). But this research did not look directly at genetics. Rather, these inferences were generated from genealogical records which go back to the founding of Quebec and maintained coherency and integrity from generation to generation. Some of the members of the same research group now have a paper out which looks at the genomics of French Canadians, and directly compares their results to that of the earlier paper. Genomic and genealogical investigation of the French Canadian founder population structure:

Characterizing the genetic structure of worldwide populations is important for understanding human history and is essential to the design and analysis of genetic epidemiological studies. In this study, we examined genetic structure and distant relatedness and their effect on the extent of linkage disequilibrium (LD) and homozygosity in the founder population of Quebec (Canada). In the French Canadian founder population, such analysis can be performed using both genomic and genealogical data. We investigated genetic differences, extent of LD, and homozygosity in 140 individuals from seven sub-populations of Quebec characterized by different demographic histories reflecting complex founder events. Genetic findings from genome-wide single nucleotide polymorphism data were correlated with genealogical information on each of these sub-populations. Our genomic data showed significant population structure and relatedness present in the contemporary Quebec population, also reflected in LD and homozygosity levels. Our extended genealogical data corroborated these findings and indicated that this structure is consistent with the settlement patterns involving several founder events. This provides an independent and complementary validation of genomic-based studies of population structure. Combined genomic and genealogical data in the Quebec founder population provide insights into the effects of the interplay of two important sources of bias in genetic epidemiological studies, unrecognized genetic structure and cryptic relatedness.

In 1760 there were 70,000 residents in the areas of Canada which were under French rule. A substantial fraction of these derived from the much smaller 17th century founding population. Today the number of North Americans with some known French Canadian ancestry numbers around ~10 million. I happen to know an individual whose great-great-grandmother was French Canadian. Using the internet it turned out that I could trace this woman’s ancestry along one line back to the countryside outside of Poitiers in the mid 16th century! Being conservative it seems that at least 5 million North Americans have overwhelming descent from the 1760 founding stock. These are the core French Canadians.

An immediate inference one might make from these background facts, the rapid expansion of the French Canadian ethnic group from a small core founding stock, is that they would have gone through a “population bottleneck.” The data here are mixed. On the one hand, there are particular Mendelian diseases associated with French Canadians. This is evidence of some level of inbreeding which would randomly increase the frequencies of deleterious recessively expressed alleles. And yet as noted in the paper French Canadians do not seem to have lower genetic diversity than the parental stock of French in the HGDP data set. Why? Because to go through a population bottleneck which is genetically significant you need a very small window of census size indeed. Tens of thousands is sufficiently large enough to preserve most of the genetic variation in the founder population which is not private to families. The sort of genetic polymorphisms which might have been typed for in widely distributed SNP chips.

But that’s not the end of the story. Though French Canadians don’t seem exhibit the hallmarks of having gone through an extreme population bottleneck as an aggregate, it turns out that in the populations surveyed there was evidence of substructure. The map to the left shows you the regions where the samples were drawn. Unlike the earlier study the sample size is smaller; this is a nod to the difference between a purely genealogical study and a genomic one. There needs to be money and time invested in typing individuals. Relatively public genealogical records are a different matter. Apparently the Gaspesia sample population were from a relatively later settlement. The urban samples naturally include descendants of local French Canadians, as well as rural to urban transplants.

As one would expect the French Canadian sample clustered with the CEU (Utah whites from the HapMap) and French (from the HGDP) in the world wide PCA. And not surprisingly they exhibited smaller genetic distance to the French than to the Utah whites (who were of mostly British extraction). Using Fst, which measures the extent of genetic variance partitioning between populations, the values from the aggregate French Canadian sample to the CEU sample was 0.0014 and to the French HGDP sample was 0.00078. The Montreal French Canadian group exhibited values of 0.0020 and 0.0012. But, it is important to observe that there was statistically significant differences between the various French Canadian populations as well (excluding the Montreal-Quebec City pairing). This may explain the existence of particular Mendelian diseases in the French Canadian population despite their lack of reduced genetic variation: there’s localized pockets of inbreeding which are not smoked out by looking at total variation statistics. Additionally, the authors conclude that not taking this substructure into account in medical genetics could lead to false positives. Inter-population differences in disease susceptibilities correlated with genome-wide differences in allele frequencies could produce spurious associations.

The population substructure can also be elucidated by extraction of the independent components of variance on a plot, as you can see to the left. Panel A represents PCA of genomic data, while panel B is an MDS derived from genealogical data. The gist here is that you’re seeing the two biggest independent dimensions of variance each data set (these dimensions explain only a few percent of the total variance). Each individual color represents a French Canadian subpopulation. It is clear that there is substructure. Individuals from each group tend to cluster with individuals from their own subpopulation. The authors take this to confirm the Fst values earlier. But to me another interesting aspect is the difference between the genomic and genealogical visualizations. The genealogical visualization looks far “cleaner” to me than the genomic visualization. Why? Genealogical records are imperfect. The rough congruence validates that the Roman Catholic Church in Quebec didn’t make records out of whole cloth, but there were likely fudges, guesses, and deceptions on the margins. One thing to remember is that even if some of the difference is due to issues with paternity, much of that sort of thing would still be within population. Of course I’m looking at this somewhat glass-half-empty. The rough congruency could be seen as a validation of the robustness of the record-keeping of French Canadian institutions over all these centuries. When there isn’t genetic data, one can use genealogical data as a substitute. At least to a rough approximation.

In the final section the paper notes that there are some peculiarities n the genetics of the French Canadians which do indicate some level of genetic homogeneity, at least by locality. To explore this issue they focus on two genomic phenomena which measure correlations of alleles, genetic variations, over spans of the genome within populations. The two phenomena are linkage disequilibrium, which measures association across loci of particular variants, and runs-of-homozygosity, which highlights genomic regions where homozygosity seems enriched beyond expectation (the former is inter-locus, while the latter is intra-locus). Both of these values could be indicators of some level of population bottleneck or substructure, where stochastic evolutionary forces shift a population away from equilibrium as measured by the balance of parameters such as drift, selection, and mutation.

To the right is a mashup of figures 5 and 6. On the left you have a figure which shows the extent of linkage disequilibrium as a function of distance between SNP. As you would expect the greater the distance between two SNPs, the more likely they’re to be in equilibrium as recombination has broken apart associations. The closer and closer two markers, the more likely they’re to be linked, physically and statistically. But there’s a difference between the two LD plots. There’s no difference between the CEU and French Canadian samples in the top panel, but there is in the bottom one. Why? The bottom panel shows LD between markers much further apart. Acadians in particular seem to exhibit more long distance LD than the other populations. This may be a sign of a population bottleneck and inbreeding. Also, please note that the Utah white CEU sample is probably relatively similar to the French Canadians in its demographic history as North American groups go. It is homogeneous and expanded rapidly from a small founder group. To the right you have in the top panel total length of ROH per individual, and the bottom length of ROH greater than 1 MB. Again, the Acadians seem to be standouts in terms of their difference from the CEU reference. Interestingly, there’s no difference between CEU, French, and the two French Canadian urban samples. I suspect this is due to the fact that in Montreal and Quebec City the distinctive inbreeding found in the other samples has been eliminated through intermarriage. ROH disappear when you introduce heterozygosity through outbreeding.

What has all this told us? From a medical genetic perspective it is implying that population structure matters when evaluating French Canadians, an Acadian is not interchangeable with a native of Montreal. In terms of ethnically clustered diseases of French Canadians, in the USA the Cajuns, it may not be that there are patterns across the whole ethnic group, but trends within subgroups characterized by long-term endogamy. I wonder if the same might be true of Ashkenazi. Is there is a difference between Galicians and Litvaks? Such regional differences among European Jews are new, but the French Canadians themselves are the result of the past three centuries. These results also seem to reinforce the Frenchness of the French Canadians. Years ago I skimmed a book on the cultural history of the people of Quebec, and the author went to great lengths to emphasize the amalgamative power of the French Catholic identity in Canada. Arguing that to some extent the roots of the community in the colonial era was something of an overblown myth. These results come close to rejecting that view. In particular the first paper, which shows the disproportionate impact that earlier settler waves have on the long term demographics of a population. A group which one could analyze in a similar vein would be the Boers, who are an amalgam of French Protestants, Dutch, and Germans, but seem to exhibit a dominance of the Dutch element culturally.

Finally, the French Canadians may give us a small window in the long term demographic patterns and genetic dynamics which might be operative on a nearby ethnic group: the Puritans of New England. Because of their fecundity it seems likely that tens of millions of Americans today descend from the 30,000 or so English settlers who arrived in New England in the two decades between 1620 and 1640. This is the subject of the Great Migration Project. With numbers in the few tens of thousands it seems unlikely that much of a thorough population bottleneck occurred with this group in a genetic sense in the aggregate. But the results from the French Canadians indicate that isolated groups can be subject to stochastic dynamics, and develop in their own peculiar directions.

Citation: Bherer C, Labuda D, Roy-Gagnon MH, Houde L, Tremblay M, & Vézina H (2010). Admixed ancestry and stratification of Quebec regional populations. American journal of physical anthropology PMID: 21069878

Citation: Roy-Gagnon MH, Moreau C, Bherer C, St-Onge P, Sinnett D, Laprise C, Vézina H, & Labuda D (2011). Genomic and genealogical investigation of the French Canadian founder population structure. Human genetics PMID: 21234765

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Last spring I posted ‘Beyond visualization of data in genetics’ in the hopes that people wouldn’t take PCA too far in assuming that the method was a reflection of reality in a definite fashion. Remember, PCA visualizations are showing you two, and at most three, dimensions in genetic variation within the data set at any given time. The fine print is important; e.g., “PC 1 15%”, “PC 2 4.5%”, etc., which points to the magnitude of the dimensions within the data. You see the largest, and likely historically most significant on a population wide scale, genetic variances, but there’s still a large remainder left over. But when I look at referrals from message boards people obviously aren’t careful with what PCA is telling them.

As an illustration, in the 23andMe user interface you can “compare genes” genes across people who you “share genes” with. This comparison operates over ~550,000 single nucelotide polymorphisms out of 3 billion base pairs (you can constrain it to traits, but I’m going to talk about the comparison to the whole data set below). For example, a man of European descent shares 83.2% with his daughter, who is Eurasian (the mother is Burmese, with some recent Indian admixture). Another man of European descent shares 84% with his daughter, whose mother is also European (in fact, both parents are western European). The “gene sharing” with other people of European descent of these two men is in the 75-74% range (for reference, a Chinese person is 71%, and Nigerian 68.5%). On the PCA plot the European and his Eurasian daughter are very far apart, while the European man and his European daughter cluster together. What you’re seeing on the PCA chart is population level information, not the genetic uniqueness within families and across parents and offspring.

To further explore this issue, I thought it would be interesting to revisit my own genetic data. If you read my previous post, you will know it is not boring. As an ethnic Bengali my ancestry comes from the northeast of the Indian subcontinent, so in addition to the “Asian” fraction which most South Asians have in the 23andMe “ancestry painting” (around 25% on average, with a range from 10-35% probably the extremes within two standard deviations from what I can tell), I likely have some southeast Asian ancestry from Burma. 23andMe has three “reference” populations it uses from the HapMap:

Asian = Chinese/Japanese
European = Northwest European
African = Yoruba

All of us get an ancestry painting which is a combination of these three. Unfortunately unless you’re a relatively straightforward combination of these three groups it isn’t always too informative. So if you’re African American you should be in luck since the two ancestral populations which you derive from are included as reference populations. On the other hand, unadmixed Native Americans tend to be about 25% European and 75% Asian, while unadmixed South Asians are 75% European and 25% Asian. That’s because the allele frequencies in these two populations have some relationship to both the reference groups, even if there hasn’t been any recent admixture (additionally, the painting presumably misses a lot that is distinctive to these groups, though 23andMe has a feature which allows people to explore possible Native American ancestry specifically).

As I told you before my ancestry is 57% European and 43% Asian. This is a very large Asian fraction for a South Asian, and after comparing notes with other South Asian 23andMe customers I’m pretty sure that my large fraction is due to having admixture from Burmese and/or Tibeto-Burman or Austro-Asiatic “Hill Tribes” to the north, south and east of Bengal. Since my family is from the east of Bengal that is not too surprising.

You know from my previous post that on the PCA plot I am near, but outside, of the main South Asian cluster. But there’s some interesting data from the gene comparison feature too. For reasons of privacy I’m not going to give you names obviously, but, I will label people by geographical origin if I know that aspect of the individual’s information. Additionally, below the comparison is mostly to Indians, and so I’m going to substitute names of Indian states for those where I have that level of specificity. I also restandardized the gene sharing value, so that the nearest individual with whom I’m sharing is 0 , and the furthest on the plot is 1 (74.5% to 73.04% if you’re curious). To add a wrinkle, I’ve added the % Asian calculated from 23andMe’s ancestry painting on the Y axis. The two images below show the results, the first includes some East Asians and a European, while the second includes only South Asians.

[nggallery id=11]

The first image is of more interest. Two points:

1 – Unlike most South Asians I have greater gene sharing identity with East Asians than with Europeans. The South Asian to whom I am closest to does not exhibit my own pattern, as they are closer to some Europeans than they are to some Chinese. In contrast, I not only unequivocally share more genes with East Asians than Europeans, but, I share more genes with some East Asians than I do with the individual from Iran, and, one South Asian from the northwest of the subcontinent and another from southern India. This last pattern is very peculiar from what I’ve been told (the other Bangaldeshi has the same tendency, though not to the same extent).

2 – There is a woman with whom I am sharing genes with from Burma. Her father, who died when she was young, had Indian ancestry, and reputedly spoke Tamil. She is ~20% European, which would make her father ~40% European. I have not seen a South Indian who is less than 65% European, so I believe that he had native Burmese admixture. If his mother was Burmese that would make his father ~80% European, which I have seen in a few South Indians, though their usual range seems to be 75-65%. Note that I am closer to her than I am to most South Asians. In contrast, the Bangaldeshi with whom I am sharing genes, and has the second highest percentage of Asian in their ancestry is about as far from this woman and he is from the Punjabis in terms of distance (in contrast, the Punjabis are about 2.5 times further than she is from my own genetic state).

7419_133883902983_699392983If I did the same plot of % Asian with gene sharing for the European man and his Eurasian daughter I would see a pattern whereby for most of the data there would be a noticeable linear pattern, the more Asian, the less gene sharing. The exception would be his daughter, who would be greatly Asian, but would be the closest by this genetic distance measure. Similarly, the Burmese woman with some Indian admixture is an outlier on my plot. The South Asians follow a southeast-to-northwest range of distance from me, with a rough, but not perfect, correspondence with Asian ancestry. Among the South Asians the individual from Bihar is an exception, just as the Burmese woman is. Why? From previous comments I’ve made I have indicated that there is a high probability of recent Burmese ancestry on my paternal lineage (specifically, my paternal grandfather, whose physical appearance is always described as atypical for a Bengali. My paternal grandmother was from a Hindu family which converted, and she looked stereotypically Bengali). Additionally, I know my mother’s maternal grandfather is from the Indian state of Uttar Pradesh, specifically, the region of Delhi. But I also know that before they were Muslim my maternal grandfather’s family were of the Hindu Kayastha caste. The individual from Bihar is a Kayastha, and for those of you who do not know, Bihar is the state just to the west of Bengal. I do not know if the Kayasthas share any deep genetic affinity or not, but I recall that Reich et al. observed a high degree of genetic evidence of endogamy in South Asia. So, just as I believe that I share Burmese-specific genetic variants with the woman of predominant Burmese origin which are not showing up in the simple ancestry estimates based on the global reference populations, I may also share Kayastha-specific variants which results in my genetic closeness to the Bihari individual. But my confidence in the latter conjecture is far weaker than in the former case.

In reviewing all I’ve said so far I suppose the moral of the story is not to trust too deeply in one set of data visualizations or summary statistics. Granted, some people have axes to grind and can find what they want in the science, my posts on Jewish genetics indicates that very strongly. But if you’re genuinely interested in patterns of variation, and your own place within the broader framework, you need to open different windows on the same data to get a truly fully-fleshed out understanding of the nature of things. If you are of an understudied population, and of somewhat mixed background, as I am, tread lightly and carefully. If you are of a well studied and characterized population, then learning you are 100% European is basically worthless (though some of the more detailed PCA’s can tell you some things).

• Category: Science • Tags: 23andMe, Genealogy, Genetics, Genomics, Personal Genomics 
Razib Khan
About Razib Khan

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