PERSPECTIVES

Genetic Characterization of Human Populations:From ABO to a Genetic Map of the British People

Walter Bodmer1

Weatherall Institute of Molecular Medicine and Department of Oncology, University of Oxford, Oxford, United Kingdom OX3 9DS

ABSTRACT From 1900, when Landsteiner first described the ABO blood groups, to the present, the methods used to characterize thegenetics of human populations have undergone a remarkable development. Concomitantly, our understanding of the history andspread of human populations across the earth has become much more detailed. As has often been said, a better understanding of thegenetic relationships among the peoples of the world is one of the best antidotes to racial prejudices. Such an understanding providesus with a fascinating, improved insight into our origins as well as with valuable information about population differences that areof medical relevance. The study of genetic polymorphisms has been essential to the analysis of the relationships between humanpopulations. The evolution of methods used to study human polymorphisms and the resulting contributions to our understanding ofhuman health and history is the subject of this Perspectives.

THE A, B, and O blood types of the ABO blood group systemwere first described by Landsteiner in 1900, the year of the

rediscovery of Mendel’s work. Landsteiner had set out to seewhether the sorts of differences between species that hadbeen detected by serological methods might also occur withinspecies (see Owen, 2008). The experiment was very simple:mix the red blood cells of one person with the serum of anotherand do this pairwise for a number of people. To his surprise, hefound that certain combinations of serum and red cells led toclumping or agglutination of the red cells. Based on the result-ing patterns of agglutination of the red cells, three types ofserum and red cells were identified: A (which agglutinates onlyB cells), B (which agglutinates only A cells), and O (whichagglutinates both A and B cells). [Landsteiner did not at firstobserve AB serum (which agglutinates neither A or B cells)because of the small size of his sample.] These types seemedto be intrinsic to the individual and did not change with time or,for example, the presence of infections. They were, as we nowknow, the inherited characteristics of an individual’s red bloodcells. Landsteiner’s work, together with many subsequent devel-opments, meant that blood transfusions became possible andeventually much safer. In addition, this simple but fundamentalanalysis was the initial stimulus for all subsequent studies of thefrequency of genetic variation in different human populations

and the use of genetic variants for characterizing genetic differ-ences among humans.

ABO, the First Human Polymorphism

It is surprising that Landsteiner did not immediately recognizethat his A and B types were likely to be inherited as simpleMendelian dominant traits. This discovery was made by VonDungerne and Hirschfeld (1910) based on a simple study ofthe inheritance of A and B in families, which showed thateach was dominantly inherited, whereas O was recessive. TheA and B types thus became the basis for the first study ofgenetic variation in human populations carried out by theHirschfelds during the First World War and published in 1919(Hirschfeld and Hirschfeld 1919). The Hirschfelds say that itwas “clear to us from the beginning that we could only attackthe human race problem on serological lines” by using the“iso-agglutinins first analyzed by Landsteiner.” By “the humanrace problem” the Hirschfelds simply meant an understandingof the relationships between different human populations.The Hirschfelds presumably took the wartime advantage ofbeing able to seek the permission of “the Directors of theMedical Services of the several armies” to examine soldiers,having been careful first to establish that their own bloodtypes had not changed over many years and were not affectedby the infections that one of them had suffered. Their results(Figure 1A), based on the assumption that A and B wereinherited as separate loci, showed substantial differences inthe frequencies of A and B between different populations and

Copyright © 2015 by the Genetics Society of Americadoi: 10.1534/genetics.114.1730621Address for correspondence: Weatherall Institute of Molecular Medicine andDepartment of Oncology, University of Oxford, Oxford, United Kingdom. Email:walter.bodmer@hertford.ox.ac.uk

Genetics, Vol. 199, 267–279 February 2015 267

major ethnic groups, with much higher frequencies of B inIndians than in the English. They concluded that there weretwo different races of humans, the As, who came from north-ern or central Europe, and the Bs, who came from India andthat these two races mixed in the middle, namely betweenEurope and India. To us, this now seems a ludicrous conclusionand shows how mistaken one can be when placing too muchemphasis on any single genetic polymorphism, a mistake thatis still sometimes made. Figure 1B gives the distribution of theB allele in Europe, Africa, and Asia from data available in 1976and shows that the Hirschfelds’ frequencies were quite compa-rable to the frequencies found nearly 60 years later using moredata and, presumably, better techniques.

The issue of the population distribution of a Mendeliandominant and how this might change from generation togeneration was a subject of much discussion in the years afterthe rediscovery of Mendel’s work. The simple, yet profound,“binomial” solution is now called the Hardy–Weinberg (H–W)law, discovered independently in the same year by Hardy(1908) and Weinberg (1908). For excellent accounts of thislaw, see the Perspectives by James Crow (1988) and AnthonyEdwards (2008). The important point is that, with randommating and in the absence of selection, the frequencies of thethree genotypes formed from two alleles at a single locus,A (with a frequency, p) and a (with a frequency, q= 12 p), arep2 AA, 2pq Aa, and q2 aa after one generation of randommating, whatever the starting frequencies, and neither thesenor the allele frequencies change in subsequent generations ofrandom mating. It turns out that this description of a popula-tion distribution, which generalizes to multiple alleles at a sin-gle locus, is remarkably robust with respect to departures fromthe conditions under which it is derived, so much so thatdepartures from an H–W equilibrium for a given pair of allelesare mostly assumed to be due to technical errors in typing.Hardy was one of the most distinguished mathematicians ofhis era, whose textbook on pure mathematics I used as anundergraduate student at Cambridge University in the earlyto mid-1950s. It seems ironic that he, the epitome of a puremathematician, should now be known far better on a world-wide basis for a simple binomial law in an applied field thanfor all his other mathematical achievements.

It was a simple application of the Hardy–Weinberg law tothree alleles that led Bernstein (1925) to the correct conclu-sion that the ABO system was determined by three alleles,A, B, and O, at a single locus rather than by alleles at twodifferent loci (see the Perspectives article by Crow (1993) foran excellent account of this).

The establishment of centers for emergency blood trans-fusion early in 1940, soon after the start of World War II,gave R. A. Fisher and his colleagues an opportunity to collectextensive data on the frequencies of ABO types in Englandand Scotland (Fisher and Taylor 1940). The number of obser-vations was very large (10,969 for Scotland, 8716 for northernEngland, and 106,477 for southern England) so that the A, B,and O allele frequencies were estimated with considerable pre-cision, at least for these three broadly defined geographical

regions. Because of a relatively low frequency of A in Scotlandas compared with England and other surrounding continentalcountries, Fisher and Taylor concluded that Scotland owes itsA vs. O composition to a proto-Scandinavian group that dif-fered from modern Scandinavians. However, this conclusiondoes not agree with more recent data, again showing the risksassociated with drawing conclusions from a limited number ofgenetic polymorphisms.

One of the earliest examples of a geographically muchmore detailed study of a population was that of Wales byWatkin (1956), again using only the ABO alleles as thegenetic markers. Watkin sampled 16,760 donors from mostof Wales, almost all with Welsh surnames and with detailedinformation about where they came from. The resulting dis-tribution of A (Figure 2) shows significant north-to-southvariation with a particular hot spot of high frequency in anarea often called “Little England beyond Wales.” This isan area often considered to be different from the surround-ing area of Wales, possibly because of 9th century Vikingsettlements and because of Flemish farmers settled thereby Henry I in the early 12th century. Watkin suggested thatthe high A frequency might be associated with the Vikingsettlements, although later data have suggested that it ismore likely to be connected with the Flemish farmers. Thisagain emphasizes the need to base conclusions on severalgenetic markers.

More Blood Groups

It is clear that better genetic definition of human populationvariation required data on more than just one polymorphicsystem. The first steps in this direction came with the discoveryof new genetically determined red blood cell polymorphisms.In 1927 two new red cell blood groups, MNS and P, werediscovered and a third, Secretor, was found in 1930. However,the major expansion of the known red cell blood groups camein the 1940s and 1950s, and it provided a whole new rangeof genetic polymorphisms for the study of human populations(Race and Sanger 1950 and later editions). First among thenew groups were the Rhesus (Rh) factors, originally discoveredthrough Levine and Stetson’s (1939) description of an unusualcase of hemolytic disease of the newborn. This was caused bya mother who lacked the first described Rh factor, now knownas D, reacting to the presence of D on the red cells of herchild by making anti-D antibodies that destroyed the red cellsof her fetus. Many new Rh determinants were detected shortlythereafter by using improved red cell agglutination techniques.The results of these discoveries were elegantly interpreted byR. A. Fisher (1947; see also the Perspectives by Edwards 2007)in terms of three closely linked loci, each with two alleles(Cc, Dd, Ee) determining the corresponding antigens C, c, etc.Only antibodies to d were never found, and this was because,as was discovered much later, d represents simply the absenceof D. The interpretation of Fisher was influenced by the Swissgeneticist Ernst’s explication of the Primula incompatibilitysystem as a complex of closely linked loci (confirmed in a letter

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that Fisher wrote to Ernst in 1957). These ideas were far aheadof their time and were developed before there was even theslightest possibility of a biochemical or molecular understand-ing of complex loci or gene clusters. Fisher had an enormousinfluence on blood group research in Britain and was a majorsupporter of the work of Race and Sanger. Mourant, who hadworked closely with Race and Sanger, and under Fisher’s in-fluence, compiled with his colleagues (Mourant et al. 1954,1976) an outstanding compendium of data on blood groupsand other polymorphisms. This was the first major work to giveworldwide data on the distribution of the human blood groupsin a way that enabled a much better genetic assessment ofthe relationship between the various human populations. Itwas this source of information that enabled Edwards andCavalli-Sforza (1963) and Cavalli-Sforza and Edwards (1965)to construct the first human evolutionary tree using gene fre-quency data. This evolutionary tree was an outstandingly orig-inal piece of work that has formed the basis of all subsequentphylogenetic analyses, and it introduced the use of PrincipalComponents Analysis (PCA) for the interpretation of gene fre-quency data. The tree that Cavalli-Sforza and Edwards (1965)obtained, using data from five blood groups and 18 alleles on15 populations, is shown in Figure 3A. In their words, it showed“three branches, Europeans, Africans and Asiatics . . . splittinginto a variety of subgroups” and a “remarkable similarity be-tween the blood group map thus obtained and the geographicalmap” (Cavalli-Sforza and Edwards 1965, pp. 929–930). It wasa surprising and remarkable achievement to obtain a result thatwas so consistent with general expectations using rela-tively few genetic variants. But, once again, to go beyond thislevel of polymorphism and obtain a more precise definition of

population relationships required the discovery of many morepolymorphisms. This has depended on the development oftechniques to study polymorphisms first at the level of pro-teins and then, and most dramatically, at the level of the DNAitself.

Enzyme Polymorphisms

As the number of blood group polymorphisms increased, thequestion was raised as to whether these were a special caseof highly variable genes or whether polymorphism was amuch commoner phenomenon than was at first apparent.An answer to this question came from studying enzymes usingthe technique of starch gel electrophoresis devised by OliverSmithies (1955). This procedure separates different versions ofa protein according to charge so that variants with differentamino acid substitutions, resulting from variations in the DNAsequence, will often move to different positions on the gel.Specific enzymes are then recognized by assays that give a col-ored product at the position of an enzyme variant. There wasno a priori reason to suppose that the enzymes chosen for theseassays would be polymorphic. Harris (1966) initially studied10 human enzymes in this way and found substantial poly-morphisms in 3 of them, suggesting that at least 30% of arbi-trarily chosen protein-coding loci are likely to be polymorphic,a figure that was confirmed a few years later when moreenzymes had been studied in this way. Using analogous tech-niques, similar results were obtained by Hubby and Lewontin(1966) for Drosophila, showing that this level of polymor-phism is not unique to a given species. Thirty percent was prob-ably a substantial underestimate of the true level of protein

Figure 1 (A) The results of first ABO population study by Hirschfeld and Hirschfeld (1919) showing high A frequencies in England and high Bfrequencies in India. (B) A synthetic map of the distribution of B in Europe, Africa, and South and East Asia (from Bodmer and Cavalli-Sforza 1976).The intensity of shading is proportional to the B frequency.

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polymorphism because not all protein variants could be detectedby the techniques then used. However, this unexpectedly highlevel of polymorphism raised the question of how much varia-tion was maintained by some form of natural selection, a ques-tion that has not yet been properly answered except for thehemoglobinopathies, inherited abnormalities in hemoglobinsuch as sickle cell anemia and thalassemia, and certain enzymepolymorphisms that are strongly associated with resistance tomalaria.

The HLA System and Disease Associations

Another measure of inherited variability comes from the ob-servation that, between any two individuals, essentially theonly graft exchanges, for example, of skin or kidneys, thatsurvive without medication are those between identical twins.This phenomenon indicates that there must be many inheriteddifferences between any two individuals that the recipient’simmune system recognizes on the donor tissue as foreign.The major complex genetic system that controls these differ-ences, now known in humans as HLA, was discovered throughthe use of sera from women who had had children and madeantibodies against their children’s antigens inherited from thefather. These differences were recognized by the agglutinationof white blood cells rather than red blood cells. The maternal

sera, however, mostly contained many antibodies, and it wasonly through the use of statistical 232 analyses of the patternsof many reactions on many individuals that it became possibleto recognize specific, dominantly inherited HLA types (Van Roodand Van Leeuwen 1963; Payne et al. 1964). The large amountsof resulting data immediately imposed the need for computers,not only for recording the data but also for analyzing them.The number of antigens so recognized increased enormouslythrough the development of a microcytotoxicity assay that wasboth much more reliable than the agglutination assay andmuch easier to do on a large scale (Terasaki and McClelland1964). The development of our understanding of the HLAsystem also depended hugely on strong international collab-oration stimulated by a series of international collaborativeworkshops that started in 1964 and have continued to thepresent. Even before the advent of DNA technology, six closelylinked HLA loci had been identified with a total of .100alleles, making it at that time by far the most polymorphicsystem known and providing almost as much information onhuman genetic variability as the combination of all the otherthen-recognized polymorphic loci.

By the early to mid-1970s, some of the major associationsbetween HLA types and specific diseases, including HLA B27with ankylosing spondylitis, had been described. These as-sociations simply came from the observation that a givenHLA type, such as B27, was much commoner in people witha disease, in this case ankylosing spondylitis, than in the generalpopulation. The associations led to an emphasis on the role oflinkage disequilibrium—the tendency for alleles at closely linkedloci to remain together on the same chromosome—as an expla-nation for these HLA and disease associations. Thus the disease-associated HLA type might simply be a marker for thefunctionally relevant genetic variation at a locus closely linkedto HLA. Linkage disequilibrium (D) was first analyzed byJennings (1917) and Robbins (1918) and shown to depend onthe recombination fraction, r, between two loci and the time, n,in generations since particular alleles at the two loci first cametogether, according to the formula Dn = D0 (12 r)n. Here Dn isthe linkage disequilibrium at generation n, and D0 that at gen-eration 0. Thus, the extent of association between alleles attwo linked loci declines at a rate (12 r) per generation, so thateventually even two closely linked alleles, and the effects thatthey produce, will no longer be associated as (12 r)n becomesvery small after a large number of generations, n. Thesebasic ideas underlie all Genome-Wide Association Studies(GWAS), which depend on having appropriate control pop-ulations to match the disease groups being studied for thepresence of genetic variants that associate with the partic-ular disease under investigation (see, e.g., Bodmer andBonilla 2008).

When I first started to analyze the factors controlling thelevel of linkage disequilibrium in Cambridge around 1960,Fisher was still from time to time in the department. I rememberon one occasion discussing linkage disequilibrium associationwith him. His view was that this would not be a very importantphenomenon because there were not enough polymorphisms

Figure 2 The distribution of blood type A in Wales (Watkin 1956). Notethe high A frequency in “Little England beyond Wales” in southwestWales.

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that were sufficiently closely linked for this association to matter.This was in spite of the fact that just one short paragraph inFisher’s classic 1930 book, The Genetical Theory of Natural Selec-tion, was the stimulus for my work and that linkage disequilib-rium played a key role in his interpretation of the Rh bloodgroup system. It was not until the advent of DNA-based poly-morphisms, as discussed below, that this negative view of theoverall importance of linkage disequilibrium had to change.

Worldwide Patterns of Genetic Variability

Even by the mid-1960s, it had already become clear that therewas a need for a book on human genetics that explained theunderlying theory of population genetics as applied to humandata, a book focused on approaches to analyzing the rapidlyincreasing amount of human genetic population data that wasbecoming available. This is what Cavalli-Sforza and I aimed todo in producing The Genetics of Human Populations (Cavalli-Sforza and Bodmer 1971). This book contained the first readilyavailable source of Cavalli-Sforza and Edwards’ evolutionarytree; the first textbook description of the HLA system and ananalysis of the linkage disequilibrium between the alleles of itsvarious loci; and an attempted summary of data of the main

polymorphisms then known in the three major ethnic groups ofEurope, Africa, and East Asia.

The HLA international workshops provided a major sourceof data on human genetic variability. The 1972 workshop, forexample, was wholly devoted to collecting good worldwidedata on HLA and other genetic variability (see Dausset andColombani 1972). These sources of information were the basisof the stimulating suggestion of Ammerman and Cavalli-Sforza(1973, 1984) that the pattern of overall changes in gene fre-quency across Europe indicated that the advance of agriculturewas best explained by demic diffusion of agriculturalists fromthe fertile crescent in the Middle East, following the model ofFisher’s wave of advance of advantageous genes, rather thanby purely cultural transmission. Such a pattern of advancewould have gradually diluted out any genetic variation char-acteristic of the people at the origins of agriculture in the fertilecrescent �10,000 years ago. In other words, by the time, say,that agriculture came to the British Isles �6000 years ago,there would probably no longer have been any significant ge-netic input from the original farmers. Although cultural trans-mission by a few skilled individuals might also result in anagricultural population in the British Isles with few geneticcharacteristics of the original farmers, it would not have resulted

Figure 3 (A) Cavalli-Sforza and Edwards (1965) published the first evolutionary tree of human populations based on gene frequency data for five bloodgroup systems. (B) An updated evolutionary tree based on 42 populations and 49 polymorphic systems (Cavalli-Sforza et al. 1988, 1994).

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in the clinal pattern of gene frequency variation observed acrossEurope.

The book by Cavalli-Sforza et al. (1994) gives a remark-able, magisterial summary and analysis of all the suitablegenetic data on human populations from blood group, en-zyme, and HLA polymorphisms that were available in 1986.It took Cavalli-Sforza et al. (1994) 8 years to accumulate thedata and another 7 years to do the analysis and write up theresults. Their aim was to bring together the genetics withthe history, archaeology, and linguistics to reconstruct theevolution of modern human populations. The result remainsa most valuable source of information on the known factorsthat have shaped the genetic and cultural structure of hu-man populations throughout the world. Figure 3B, takenfrom Cavalli-Sforza et al. (1988), shows an updated versionof the evolutionary tree first produced by Cavalli-Sforza andEdwards in 1963. It is based on 42 populations using dataon 120 alleles from 49 genes and genetic systems, with justtwo loci from HLA providing 25% of the overall information.The large amount of information used to create this tree clearlyleads to a much finer definition of the relationship between thedifferent human populations than was present in the originaltree, but the basic shapes of the trees are surprisingly similar.One significant difference is the clear, major separation be-tween African and other populations in the later tree, probablybecause of much better sources of African data.

Variation Within and Between Populations

Traditional approaches to the characterization of human pop-ulation differences, especially as developed in the 19th andearly 20th century, were generally influenced by racial pre-judice and emphasized obvious outward features, such as skincolor and facial features. This led to a view that the majordifferences between humans existed between rather thanwithin ethnic groups. The genetic data used for representingthe relationship between human populations in an evolution-ary tree can also be used for a quantitative assessment ofthe relative extent to which genetic variation for objectivelydefined genetic polymorphisms occurs within as compared tobetween populations. A large population can generally bethought of as being subdivided into a series of smallerpopulations. If, for simplicity, we assume that there is ran-dom mating within each of the smaller populations, but noexchange between the populations, then it can be shown thatthe between-population variance for a two-allele polymor-phism with different allele frequencies pi and 1 2 pi in thevarious populations is the following: Variance (pi)/p(1 2 p),where p is the overall average gene frequency (see, e.g., Cavalli-Sforza and Bodmer 1971). This quantity will be 0 if all pop-ulations have the same allele frequencies and 1 if all pi areeither 0 or 1. The values of Variance (pi)/p(1 2 p) calculatedby Cavalli-Sforza (1966) for a total of 15 alleles for nineblood group and protein polymorphisms lay between �0.05and 0.4 (average 0.148) for numbers of ethnic groups rang-ing from 25 to 125, indicating that much more variation for

these markers occurred within than between populations.Lewontin (1972) gave the results of a similar set of calcu-lations using 17 polymorphic systems for 170 populationsgrouped into seven races, but using a different measure ofheterogeneity. He calculated that the average proportion ofwithin-population heterogeneity was 0.854, that betweenpopulations within races was 0.083, and that between raceswas 0.063. Quite independently of Lewontin’s calculations, in1975, Bodmer (1975) did similar calculations using data fromDausset and Colombani (1972) on seven polymorphic sys-tems (including two HLA loci) and 21 alleles for 27 popula-tions representing all the major ethnic groups. The average ofVariance (pi)/p(1 2 p) for differences between eight ethnicgroups was 0.11, leaving 89% of the overall genetic variationwithin populations. Thus, these three separate and indepen-dent assessments came to essentially the same conclusion,namely, that between 80 and 90% of the genetic variationin the human population was between individuals withinpopulations or ethnic groups and only 10–15% was betweenthe groups. Lewontin (1972) used this result to argue thatthere was, therefore, no meaning or value to defining humangroups on the basis of genetic variation, concluding his articlewith the statement: “Human racial classification is of no socialvalue and is positively destructive of social and human rela-tions. Since such racial classification is now seen to be ofvirtually no genetic or taxonomic significance either, no jus-tification can be offered for its continuance’’ (Lewontin 1972,p. 397). The opening statement of this last sentence is clearlyscientifically wrong because the same genetic data have beensuccessfully used to describe relationships between humanpopulations in terms of evolutionary trees (for a clear expla-nation of this, see Edwards 2003). Furthermore, as I will de-scribe later, it has now become possible in many cases toidentify with reasonably high probability where people comefrom even within the United Kingdom. From a purely medicalpoint of view, it can be very important to know a patient’sethnic background because that can influence whether she orhe should be screened for a particular genetic disease, includ-ing Tay Sachs in Ashkenazi Jewish populations and sickle celltrait for those with a West African background. Moreover, it isno good having a bone marrow donor program that does nottake into account ethnic background because the HLA typesvary substantially between the various major racial groups.Not taking this variation into account means that certaingroups would be discriminated against with respect to donoravailability. Finally, most people are interested in where theycome from, beyond what their genealogy can tell them, andonly genetics can eventually answer that. The mistake is notthat of studying the genetic relationships between differenthuman population groups, but that of assigning a value, goodor bad, to groups defined in this way.

The DNA Revolution

Over the past 25 years, the increasing ability to study poly-morphisms at the DNA level has completely transformed the

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genetic study of human populations and the search for theinherited basis of common multifactorial human diseases.

The first approach to identifying DNA polymorphisms camefrom a combination of the development of DNA cloningtechniques, the use of site-specific restriction enzymes to pro-duce defined fragments of DNA (for which W. Arber,D. Nathans and H. Smith shared the Nobel prize in 1978),and the Southern blotting technique (Southern 1975). Thesetechniques permitted the identification of restriction fragmentlength polymorphisms (RFLPs) as bands of different sizesafter cleavage of a DNA preparation with a given restrictionenzyme, electrophoresis in agarose gels, and blotting ontoa nitrocellulose membrane to enable identification of specificDNA sequences by annealing of defined, labeled DNA frag-ments. Band sizes between individuals will vary if there arebase substitution polymorphisms at sites recognized by a re-striction enzyme, which prevent cleavage. The first applica-tion of RFLP analysis came from Kan and Dozy (1978a,b),who found an RFLP closely associated with the sickle cell HbSmutation. This RFLP could then be used for prenatal diagno-sis in families that segregated for HbS alleles using DNA fromthe fetal cells in amniotic fluid. The hemoglobin genes wereideal candidates for these early studies because of the easewith which DNA clones for these genes could be isolatedusing the abundant amounts of the corresponding messengerRNA (mRNA) found in red blood cells.

When it became clear that the number of recognizableRFLPs could be very large, Solomon and Bodmer (1979)suggested that it should be easy to find RFLPs that couldcover the whole human genome at reasonably close geneticintervals. Such a set of markers, they said, “could revolu-tionize our ability to study the genetic determination ofcomplex attributes and to follow the inheritance of traitsthat are so far difficult or impossible to study at the cellularlevel” (Solomon and Bodmer 1979, p. 923). This idea wasdeveloped further in my 1980 Allan Award lecture to theAmerican Society of Human Genetics (Bodmer 1981) to in-clude the notion of positional cloning and was also enlargedupon by Botstein et al. (1980).

Using restriction enzyme digests, Allan Wilson and hiscolleagues (Cann et al. 1987) analyzed mitochondrial DNAvariation in 147 samples from five major regions of theworld. Mitochondrial DNA was a convenient target for studybecause of its distinctive and abundant presence outside thenucleus, its lack of recombination, and the fact that it ismaternally inherited. The evolutionary tree obtained fromtheir analysis (Figure 4A) for the first time clearly supportedan origin for Homo sapiens in Africa, the “out of Africa”hypothesis. This is seen by the major distinctive branch ofthe tree coming from Africans. Cann et al. (1987) also pro-vided a tentative estimate of 90,000–180,000 years sinceH. sapiens first migrated out of Africa, a figure that wasconsidered quite low at the time. Some more recent geneticdata suggest the figure could be �60,000 years. Although theinterpretation of the Cann et al. (1987) data was subse-quently disputed, their basic conclusion of an “out of Africa”

hypothesis has subsequently been extensively confirmed us-ing a variety of other sources of information, including evenclassical blood group, enzyme, and HLA markers (Cavalli-Sforza et al. 1988).

Variation of the male-determining Y chromosome is thecounterpart to mitochondrial DNA variation and has beenthe subject of many DNA-level studies over the last 15–20years. Figure 4B shows an evolutionary tree based on theanalysis of 167 DNA-detected variations in the nonrecom-bining part of the Y chromosome, falling into 116 haplotypes(combinations of mutations occurring together on the samechromosome segment) using 1062 globally representativesamples of males (Underhill et al. 2000). There is a clearseparation into three major groups: African, East Asian withNew Guinea, and Australian. Europe and the Middle Eastare together with India and Pakistan on the margin. NativeAmericans lie between Eurasians and East Asians whileSudanese, Ethiopians, and Sardinians lie between the Afri-cans and the Europeans. Estimates of the time of separationbetween Africans and other populations were on the shortside at �44,000 years, but in a 95% range of 35,000–89,000years.

Polymerase Chain Reaction, Short Tandem Repeats,and SNPs

The development of the polymerase chain reaction (PCR)opened up a whole new range of possibilities for detectingDNA polymorphisms. The first systematic application to humangenetic mapping and population studies was the discovery thatshort nucleotide repeats, which are very common throughoutthe human genome, tend to be very polymorphic with respectto the numbers of repeats, probably because they are subject toa relatively high mutation rate due to replication slippageerrors. This variation can easily be detected by electrophoresison a gel as variable-sized fragments, the so-called short tandemrepeats (STRs) or microsatellites, of PCR products encompass-ing the repeat sequence (see, e.g., Weber and May 1989).

With a view to stimulating the study of human geneticpolymorphisms in suitably collected samples from popula-tions throughout the world, Luca Cavalli-Sorza around 1991suggested that this should become an activity of The HumanGenome Organization. There were those at that time whowere concerned that this proposal had a connotation ofracialism. This view was most strongly, and even aggressively,promoted by a group of activists claiming to represent in-digenous peoples, who expressed a concern that such people’sDNA might be exploited for commercial reasons, which wasnever intended nor has ever, to my knowledge, happened. Aftera series of discussions over the next several years, a concreteproposal was developed for collecting a series of lymphoblas-toid cell lines as a permanent source of unlimited amounts ofDNA, derived from a collection of.1000 individuals who wererepresentative of most of the major regions of the world. Thisidea was fortunately strongly supported by Jean Dausset, thefounder of Centre d’Etude du Polymorphisme Humain, and by

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his then colleague, the late Howard Cann, who very effectivelyimplemented the proposal (Cann et al. 2002; see also Cavalli-Sforza 2005). It is these samples that were used for the firstworldwide study of human genetic variation (.1000 individu-als from 52 populations representing five major regions of theworld) tested on 4199 alleles based on 377 microsatellite poly-morphisms spread throughout the genome (Rosenberg et al.2002). The results of the data analysis using a novel approachto the detection of population structure (Pritchard et al. 2000)are shown in Figure 5A. This analysis groups individuals intoK clusters in such a way as to maximize the amount of variationbetween clusters relative to that within. For any given K, theclusters are distinguished by colors, and each small vertical linerepresents a single individual who may be a mixture of contri-butions from more than one cluster. With K= 5, the separationinto the major regional population groups of the world is veryclear, going from Africa (brown) through Europe and the Mid-dle East and Central Asia (blue) to East Asia (pink and purple),Melanesia (green), and America (only purple). There areclearly minor variations within this overall classification, whichis hardly surprising given the relatively sparse representation ofdifferent geographical areas. The United Kingdom, for example,was represented by only 16 individuals contributed from my

laboratory, all from Orkney, a series of islands off the northeastcorner of Scotland.

The next major leap in technology has been the ability,using microarray technology originally developed by PatrickBrown and others for mRNA expression analysis, to typethousands of DNA samples for $1,000,000 SNPs (see, e.g.,Matsuzaki et al. 2004 for an early version of this). AlhoughSNPs are intrinsically less variable than STRs, they are morestable, their detection is easily automated, and with suchlarge numbers, the amount of genetic information per in-dividual is no longer limiting. To apply this technology toobtaining a finer distinction between human populations,a key requirement is to have reasonably sized samples ofindividuals with finer definition of their geographic origins.An example of this is the analysis of European populationspublished by Novembre et al. (2008) who typed .3000individuals with an array of .500,000 SNPs. However, tobe sure of having individuals who represented their countryof origin, which was their finest-scale geographic definition,individuals were removed if the country of origin was notclear-cut, for example, having parents or grandparents fromdifferent countries. They thus ended up using the data ononly 1387 samples. In addition, because they were using

Figure 4 (A) The first evolution-ary tree based on mitochondrialDNA variation, suggesting a com-mon origin for all samples inAfrica (Cann et al. 2002). (B)An evolutionary tree based onDNA-level variation found in thenonrecombining part of the Y chro-mosome (Underhill et al. 2000).

274 W. Bodmer

PCA to analyze the data on allele frequencies, which wouldbe valid only if all the frequencies were statistically indepen-dent, they removed SNPs that showed strong pairwise link-age disequilibrium, leaving 197,146 loci for analysis of the500,000 tested. The plot of the data for the first two axes ofthe PCA analysis is shown in Figure 5B. Here individuals arecolored according to their country of origin, as shown on theinset map. There is a reasonably good correspondence be-tween the two-dimensional genetic plot and the geographyin the sense that individuals from the same country tend tocluster together in the PCA plot with some interesting sep-arations such as between Ireland and Great Britain, but alsosubstantial overlaps, such as between Switzerland, France,and Belgium and between Germany and the Nordic coun-tries. In Switzerland, however, there was evidence of being

able to distinguish among French, German, and Italian speak-ers. The authors suggest that they could place 50% of indi-viduals within 300 km of their reported origin. While theseresults are a substantial advance on the broader, largelyworldwide regional classifications that had been obtainedbefore, they do not yet approach resolving finer differenceswithin a country.

People of the British Isles

The need for good control populations for GWAS disease asso-ciation studies searching for potential markers of susceptibilityto common chronic diseases has stimulated the carefulcollection and detailed genetic analysis of populationswithin countries. Within the United Kingdom, for example,

Figure 5 (A) Population structure analysis of 1056 individuals from 52 worldwide populations using 377 microsatellite polymorphisms. For each K,different colors represent the different clusters. K = 5 seems to give the best representation of the regional variation for the 52 populations (Rosenberget al. 2002). (B) Two-dimensional plot of the first two principal components from a PCA of �200,000 SNPs tested on 1387 samples from throughoutEurope (Novembre et al. 2008).

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the Wellcome Trust has supported major GWAS studies. Toaccompany this support, the Wellcome Trust provided supportfor my laboratory, with the help of Peter Donnelly and manyothers, to undertake a genetic study of the British population(People of the British Isles or PoBI) at a much finer level thanhad been done before.

Our first requirement was to have a sample that was trulyrepresentative of the different parts of the United Kingdomand hence uncomplicated by recent migrants into those areas.To do this, we aimed to sample volunteers only in rural areasand only those for whom all of whose four grandparentscame from approximately the same area. Because of the ex-pectation that any regional genetic differences within theUnited Kingdom would necessarily be small, we required thatat least �500,000 SNPS should be analyzed. Finally, based onan analysis of the then available HLA data by Bodmer (1980),it was clear that taking into account linkage disequilibriumwould give a much higher resolution of genetic variation thanusing only independent SNP data, in contrast to the approachused by Novembre et al. (2008). The PoBI study has taken.9years to come to its initial conclusion (Winney et al. 2012;Leslie et al. 2014). The essential results of an analysis of�500,000 SNPs on 2039 individuals who had been typedfor all the SNPs and who had satisfied our inclusion criteria,using a novel clustering algorithm (fineSRUCTURE, Lawsonet al. 2012) that takes linkage disequilibrium into account,are shown in Figure 6. Individuals were clustered only on thebasis of their genetic similarities and membership of a clusteris indicated by a unique symbol combining color and shape.Individuals were then plotted on a map of the United King-dom at positions corresponding to the mean position of theplace where their grandparents were born. The most strikingresult shown by the map in Figure 6, which includes 17 clus-ters, is the remarkable correspondence between the geneticclusters and their geographic location. Even Cornwall andDevon, the two neighboring counties in the southwest cornerof England, are quite clearly separated. The most different ofall the clusters from the rest of the United Kingdom is thatfound in Orkney, which clearly corresponds to the existenceof a Norse Viking earldom in Orkney from 873 to 1468.

The population of the United Kingdom has a relativelysimple history compared to the rest of Europe. Great Britainwas first inhabited by modern humans after the last ice age,some 12,000 years ago, with the next major perturbationbeing the arrival of agriculture �5000–6000 years ago. Thencame the Roman occupation from 43 to 410 CE, followedsoon after by the Saxons and related groups. The Vikingscame �800–950 CE and finally there was the Norman inva-sion of 1066, after which there has been no further majorinvasion of Great Britain. Of all the migrations after the initialsettlement, historical and archaeological evidence suggeststhat only the Saxon and Viking invasions are likely to havehad a major impact on the genetics of the indigenous popu-lation of the British Isles.

To relate our genetic clustering and its geographic structureto this known history of the British population, we used

relatively extensive European data for an admixture analysis.This showed that Orcadians had by far the greatest extent ofNorwegian admixture, as might be expected from the existenceof a Norse Viking earldom in Orkney for nearly 600 years. Theanalysis also suggested that the Welsh clusters probablyrepresent the closest to the original settlers of the British Isles,since they had the least amount of admixture from putativeViking or Anglo-Saxon sources. The existence of mostlyquite well-separated clusters suggests a relative stability ofthe British people over quite long periods of time. The largecluster of red squares, which covers much of southeastEngland and includes East Anglia, the Midlands, and someway up the east coast, and which has the largest putativeAnglo-Saxon element, shows the most remarkable homoge-neity. Preliminary estimates of admixture times, using rates ofdecay of shared haplotypes, agreed to within 100–200 yearswith known historical events.

We have in this study achieved amuch finer genetic structuralanalysis than has been achieved for any other population so far.There was even a hint in our data of “Little England beyondWales,” although denser sampling in that area would be neededto confirm that. There is no doubt that, in spite of the very smallamount of genetic difference that defines our clusters, in manycases we would be able to assign an individual of unknownorigin to their cluster location. This is illustrated by the fact thatwe found an individual sampled in the northeast of Scotlandwho was placed in Blackburn near Manchester. It turned outon further checking of the paper work that this individual camefrom a Blackburn that was in Aberdeenshire, and not the better-known Blackburn in Lancashire.

We attribute our success in this very fine genetic structureanalysis to (i) the careful collection of the samples, (ii) theuse of a large genome-wide panel of genetic markers, and (iii)the use of a more sophisticated analysis than PCA or theoriginal STRUCTURE program, which takes into accountlinkage disequilibrium.

Conclusions

The stepwise transition from simple ABO typing using redblood cell agglutination to the sophistication of the sort offine-structure analysis achieved in the PoBI study has dependedon a series of advances in techniques—from improved ag-glutination assays, starch gel electrophoresis, and the micro-cytotoxicity tests that enormously enlarged the scope of earlyHLA typing through to the various dramatic improvements inDNA technology. Each of these developments increased therange of human polymorphisms available for study, and sogradually increased the precision of our ability to analyze thegenetic relationships between human populations and relatethem to the known history and the developing knowledge ofhuman origins, especially the newer archaeological discover-ies. At the same time, the ability to analyze large numbers ofpolymorphisms at the DNA level has enabled larger and largerstudies searching for specific genetic variants that have signif-icant effects on susceptibility to common chronic diseases.

276 W. Bodmer

These studies depend on the availability of good control dataon the frequencies of polymorphisms in the populations inwhich the disease association studies are carried out.

A positive by-product of this work has been support forlarge-scale human population genetic studies. These are,however, unfortunately often not done on the sorts of carefullyselected samples that are needed to discern detailed humanpopulation relationships and their origins. A concomitant tothe development of laboratory techniques for the analysis ofhuman genetic variation has been the extraordinary develop-ment of computing power that is needed not only for thestorage and handling of large bodies of data, but also for thehighly sophisticated analyses that are now needed to extractthe maximum amount of information from a large body ofdata on gene frequencies. The limitations are no longer inthe technologies for analyzing genetic variation in humanpopulations, but in the availability of carefully collected and

documented samples and in the costs of large-scale studies.Undoubtedly the next step, which is already beginning tohappen, will be to do whole-genome DNA sequencing on largenumbers of populations putting an even greater pressure onthe need for adequate resources. There will, however, alwaysbe the need to collect samples for analysis with great care andattention paid to the purpose of a study. We should surely hopeto see studies with the resolution of what has been achieved inthe United Kingdom done in the rest of the world, moreespecially in Africa, from where we all trace our origins.

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Communicating editor: A. S. Wilkins

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