Human Molecular Genetics, 2001, Vol. 10, No. 20 2199-2207
© 2001 Oxford University Press
Population genomics: a bridge from evolutionary history to genetic medicine
Department of Human Genetics and 1Department of Pediatrics, University of Utah Health Sciences Center, Salt Lake City, UT 84112, USA
Received June 26, 2001; Accepted July 12, 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONTHE DISTRIBUTION OF HUMAN...BIOMEDICAL APPLICATIONS OF HUMAN...CONCLUSIONSREFERENCES |
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Studies of human genetic variation are making contributions in several key areas. Evolutionary genetic studies yield critical clues about the histories of human populations, and they provide substantial support for an African origin of modern humans. The analysis of genetic variation has formed a foundation for DNA-based forensic applications. And, as attention is focused on locating genes underlying complex diseases, it is becoming clear that a better understanding of genetic variation will help to guide gene-mapping efforts. Population genomics, the large-scale comparison of DNA sequences, is now beginning to provide new insights in these areas. We review some of the general patterns of human genetic variation, and we show how our knowledge of these patterns can aid in the mapping and cloning of disease-causing genes.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONTHE DISTRIBUTION OF HUMAN...BIOMEDICAL APPLICATIONS OF HUMAN...CONCLUSIONSREFERENCES |
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The discovery of the ABO blood group at the inception of the 20th century is often thought to mark the beginning of the modern study of human genetic variation. As the 21st century begins, the Human Genome Project has set the stage for a new beginning in the assessment of our species’ variation. We are now in a position to assemble and analyze human variation at the level of whole sections of the genome, and multiple sequences of entire human genomes will probably be available in the near future. ‘Population genomics’ promises to add an exciting new dimension to our understanding of human genetic variation.
Why, aside from our natural interest in similarities and differences, should this topic be of such interest? First of all, studies of human genetic variation have helped to answer fundamental questions about the origins and evolution of our species (1–4). The quantity and quality of these studies have increased in direct proportion to the increased quantity and quality of DNA data available during the past 2 decades. In addition, these studies have helped to formulate rational decisions regarding the use of DNA variation in the forensic arena (5,6). Finally, the relevance of genetic variation to biomedical studies is becoming ever more apparent (7). Because the genetic variation responsible for disease is but a subset of genetic variation in general, it is essential to understand the evolutionary processes underlying this variation if we are to identify genes that cause complex diseases. Furthermore, it is clear that allelic variation affects responses to key environmental agents, including many drugs (8–11).
This review will provide a general outline of the progress to date in our understanding of human genetic variation and its evolutionary and biomedical applications. Special emphasis will be placed on the ways in which evolutionary studies can guide and inform efforts to map and clone disease-causing genes.
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THE DISTRIBUTION OF HUMAN GENETIC VARIATION: EVOLUTIONARY IMPLICATIONS |
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TOPABSTRACTINTRODUCTIONTHE DISTRIBUTION OF HUMAN...BIOMEDICAL APPLICATIONS OF HUMAN...CONCLUSIONSREFERENCES |
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Thousands of publications have reported results based on human mitochondrial DNA (mtDNA) variation, Y chromosome variation and various types of autosomal variation. This work has permitted several broad conclusions about the origins, evolution and affinities of Homo sapiens.
The effective human population size is relatively small, approximately 10 000
Effective population size (Ne) reflects the amount of genetic variation in a population if it were, among other things, panmictic and constant in size. Ne can be estimated on the basis of autosomal genetic diversity as Ne = 
/4µ, where is nucleotide diversity and µ is the mutation rate. Because the human population does not conform to assumptions such as constant size and panmixia, our effective size, as measured by genetic diversity, is far less than our census size. An estimate of Ne 
10 000 is supported by numerous studies of mitochondrial DNA (12), Y chromosome variation (13) and various types of autosomal polymorphisms (14–17). It is instructive to compare the human effective population size of 10 000 with that of the chimpanzee, our nearest evolutionary relative. Despite a much lower census size than the human (approximately 100 000–200 000), the effective size of the chimpanzee population is approximately 35 000 (18).
The human population probably underwent a substantial expansion in size during the late Pleistocene
If a population undergoes a bottleneck in size, much of its genetic diversity is lost, and many generations are required to regain diversity. Thus, one explanation for our relatively small effective size is that the human population was far smaller at some time in the past and then increased rapidly in size. This conclusion is suggested, for example, by the strong excess of rare variants seen in mtDNA sequence data (Fig. 1). In a rapidly expanding population, the action of genetic drift (which would otherwise tend to remove rare variants) is limited, resulting in an excess of rare variants in the present-day population. By counting the average number of nucleotide differences between pairs of individuals, it is possible to estimate the date of the population expansion (i.e. the more recent the expansion, the fewer the nucleotide differences observed between pairs of individuals) (19,20). Extensive analysis of mtDNA data indicates that the human population underwent a rapid expansion in size, from perhaps several thousand or so individuals, 
70 000 years ago (21,22).
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A potential difficulty with this interpretation, however, is that natural selection for a specific mtDNA variant would exert exactly the same effect as a demographic population expansion: the frequency of one mtDNA type could have increased because of selection and may now be present in nearly all humans. Natural selection is unlikely to affect other genetic systems in the same way, so the demographic expansion argument would be reinforced by similar patterns in other types of genetic systems. Indeed, such evidence now exists for Y chromosome microsatellites (23) single nucleotide polymorphisms (SNPs) (24,25) and for autosomal microsatellites (26–31). The autosomal microsatellite data are especially powerful because they represent dozens of independent, non-coding regions. The specific results of these studies vary somewhat (e.g. the estimated dates of the expansion differ in some cases, and some studies find evidence for expansions in specific populations, such as Africans, whereas others do not). However, analyses of these different types of genetic systems all detect the signature of a relatively recent population expansion, thereby reinforcing the argument based on mtDNA data.
What can the new population genomics data contribute to this question? Like the mtDNA and Y chromosome polymorphisms, SNPs derived from these autosomal DNA sequences offer the advantages of a lack of ascertainment bias and, in many cases, little or no recombination within DNA sequences. [Concerns were raised recently about the possibility of recombination in the human mitochondrial genome (32) but these have now been discounted (33–35).] Table 1 shows that the picture appears to be less clear for these data, with many of the regions failing to exhibit an excess of rare alleles (which would be denoted by a significant negative value of Tajima’s D statistic). Most of these regions, however, include coding DNA. In contrast, many non-coding regions do exhibit an excess of rare alleles, consistent with the expansion hypothesis (36–43). [Not all of these analyses reached statistical significance, however, and non-coding regions closely linked to genes could be affected by genetic hitchhiking (44).] It has been proposed that balancing selection, which eliminates rare alleles in coding DNA but would have less effect on non-coding DNA, could be responsible for this pattern (45,46). The common objection to widespread balancing selection is that it would impose an enormous genetic load, but there are models in which relatively high levels of balancing selection could persist (47). The expansion hypothesis, which remains somewhat controversial (48), will ultimately be resolved by the analysis of additional population genomic data in humans and in other species with differing demographic histories.
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Human genetic diversity is relatively lower than that of many other species
A convenient estimate of sequence diversity is given by
, which measures the average proportion of nucleotide differences between two homologous DNA sequences chosen randomly from a population. Li and Sadler (49) estimated as approximately 1/1000 for humans. This estimate has held up remarkably well through the past decade (36,43,48,50) and has recently been reinforced by an estimate of 7.5 x 10–4 in 1.4 million SNPs obtained by the International SNP Map Working Group (51) (see also the summary of values in Table 1). The low level of human genetic diversity, relative to that of other species, is consistent with a recent evolutionary origin of our species (18,49,52).
The percentage of genetic diversity seen between the major continental populations is 10–15%
If the human population is subdivided into the major Old World continents of Africa, Asia and Europe, 85–90% of genetic variation exists within these subdivisions, and only 10–15% of variation exists between them. This estimate is highly consistent for many types of autosomal systems, including protein polymorphisms, blood groups, restriction fragment length polymorphisms, microsatellite polymorphisms and SNPs (for recent reviews and comparisons see 53,54). Although the amount of diversity seen between the major continents is relatively small, it becomes statistically significant when a large number of polymorphisms is assayed (54), and populations do cluster by continent (Fig. 2). In addition, the variation between populations within continents is substantially less than the variation between continental populations (Table 2 and Fig. 2).
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Genetic evidence supports an African origin of modern humans
The conclusion that anatomically modern humans evolved first in Africa is based on several major findings. First, excess African genetic diversity is observed in many different types of systems, including mtDNA (55), Y chromosome DNA (25,54,56–58; but see 23), autosomal microsatellites (59–63), autosomal Alu polymorphisms (64) and nuclear DNA sequences (36,37,48,50,65–68). This pattern is consistent with a scenario in which a small portion of the African population, containing a subset of African genetic variation, left Africa some 100 000 years ago to colonize the rest of the world. [However, excess African diversity can also be explained by a higher effective population size in Africa (69).] Secondly, when it is possible to designate the ancestral state of a series of polymorphisms (e.g. by comparison with an outgroup such as the chimpanzee), the root of the human phylogenetic tree falls nearest or within African populations (25,35,64,70–73). Thirdly, many different types of polymorphisms show that Africa contains the most genetically divergent group of populations (3,58,62,64,74,75) (Fig. 2 and Table 2). This is again consistent with the departure of a small, genetically homogeneous subset of the African population into Asia and Europe, although smaller European–Asian genetic distances could also be produced by greater gene flow between these two continents. Fourthly, linkage disequilibrium values, which can reflect the ages of haplotypes in populations, are usually lower in African than in non-African populations (76–82). Finally, and perhaps most convincingly, studies of haplotype variation show that haplotypes found outside Africa are usually subsets of the larger collection of haplotypes found within Africa (25,41,56,63,77,79,83,84).
The ‘out-of-Africa’ model of human origins is often contrasted with the ‘multi-regional’ model, in which modern humans evolved from archaic forms in several locations in the Old World. The genetic homogeneity seen in contemporary populations is thought to be the result of gene flow and natural selection (85). Although the latter model (or variants thereof) has its advocates (4,86–88), the weight of genetic evidence clearly favors an African origin of modern humans (2,3,21,89–91).
It is more difficult to test the hypothesis that humans radiating from Africa completely replaced indigenous archaic humans, such that contemporary genetic variation contains no contribution from non-African archaic populations. The lack of ancient mitochondrial or Y chromosome lineages in contemporary non-African populations (35,57) does not completely rule out archaic non-African contributions: their lineages may have simply been lost because of genetic drift. This same argument cautions us against over-interpreting the fact that ancient Neanderthal mtDNA is quite distinct from that of modern humans (92,93). Similarly, the ancient autosomal sequences found in non-African populations (17,36) do not prove archaic non-African contributions because these sequences could be the descendants of lineages that were carried out of Africa, surviving population bottlenecks that likely occurred with the African exodus. The replacement hypothesis will become better resolved as additional population genomic and fossil data are collected and analyzed.
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BIOMEDICAL APPLICATIONS OF HUMAN VARIATION STUDIES |
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Association-based gene mapping: a rationale
For some time, gene mapping based on between-locus associations [linkage disequilibrium (LD)] was largely the domain of those interested in theory and methodology. Hence it was largely ignored. This changed abruptly with the success of LD in narrowing the location of several Mendelian diseases (94–96). As frustration grows with the conflicting results of linkage-based genome scans for complex disease genes (97), increasing attention (either hopeful or critical) is being directed to association-based mapping approaches (97–105).
The rationale for using LD in this context is now well recognized: LD decays through time as a result of recombination, so it is an indirect measure of the genetic distance between loci. Furthermore, LD is potentially affected by all of the recombinants that have occurred through history, so it reflects many generations of recombination (in effect, a much larger ‘pedigree’ than could ever be assembled in a traditional linkage study). Human history is complicated, however. In addition to recombination, all of the factors mentioned above in the context of human evolutionary history—mutation, gene flow, genetic drift, population growth and population subdivision—can affect LD patterns. This is precisely why a better understanding of human genetic variation, and the evolutionary factors responsible for it, is required if LD approaches are to be successful. Here we address several relevant questions that can be better answered through studies of genetic variation.
What is the range of LD, and what does this imply for SNP density in association studies?
Early empirical studies, using relatively limited marker data, suggested that significant LD, although irregular, is correlated with inter-locus physical distance and often extends across distances of 20–50 kb (106–110) or even more (111–113). It was found that the extent of disequilibrium was affected not only by physical distance but also by distance from the centromere (114) and the presence of recombination hot spots (115). It is encouraging that more recent and extensive surveys, made possible in part by the availability of population genomic data, have been largely consistent with the earlier findings (116–120).
These empirical results can be contrasted with a computer simulation that showed that useful LD is unlikely to extend beyond 3 kb or so (103). Simulations, like most models, incorporate simplifying assumptions in the interest of clarity, and this may account for the discrepancy seen here. In particular, no allowance was made for the possible effects of natural selection against disease-causing variants, which would limit the age of the variant and thus the amount of time during which LD could dissipate. In addition, a simple model of exponential growth was assumed. Although there is evidence for an ancient human population expansion, it is unlikely that our population expanded once and only once. Surveys of population genomic data suggest that patterns of variation support neither a model of constant human population size nor one of a simple exponential expansion (48). Cyclic population bottlenecks and expansions, for example, could help to account for elevated levels of genomic LD (121). Clearly this is an area in which a better understanding of human evolutionary history and its effects on genetic variation will help to dissect the complexities underlying LD.
Considering the empirical results, an SNP density of one per 6 kb, implied by the above-mentioned simulation study, may be unnecessary in many applications. However, LD can be influenced by chromosome location (119,122), choice of population (see below), gene conversion (123), type of markers used (124,125) and possibly isochore structure (126). In addition, the statistics that measure LD have considerable variance (127,128). Consequently, patterns of LD tend to be irregular, with closely linked polymorphisms sometimes showing no LD at all (110,111,116,129,130). Thus, there is no simple rule for determining SNP density in an association study. Rather, a number of critical factors must be recognized and incorporated.
Which populations are best suited for association-based mapping studies?
Evolutionary studies of human populations have contributed substantially to our ability to choose appropriate populations for LD mapping studies. As would be expected under the out-of-Africa model, African populations tend to exhibit the lowest average levels of inter-marker disequilibrium. LD dissipates rapidly with increasing inter-marker physical distance in these populations. Higher average LD levels, persisting to greater physical distances, are usually seen in European and Asian populations (77–80,120,131). Even higher levels of disequilibrium are sometimes seen in some recently founded populations (113,118,132–134) and in populations that have undergone recent admixture (135,136).
These patterns suggest that populations founded more recently will often be most useful in detecting long-range associations between disease-causing mutations and marker polymorphisms, while ‘older’ populations could be more useful for fine-scale mapping (99,120). The genetic distances shown in Table 2, along with the relative lack of heterogeneity between populations within the Asian and European continents (Fig. 2), suggest that LD patterns may often be quite similar in most Asian and European populations. Greater heterogeneity is to be expected in African populations.
Of particular interest for LD applications are isolated populations, in which there have been notable successes in locating rare Mendelian disease-causing genes (94,95). This has spurred an interest in using isolates to map genes underlying complex diseases (102,137–139). The logic is that these populations, having fewer founders and less admixture, may present less allelic and locus heterogeneity. There are several potential problems with this reasoning, however. First, the Mendelian disorders mapped by LD approaches were caused by relatively young, rare variants; indeed, LD mapping was also successful for the major hemochromatosis mutation, which is a young variant but distributed in an outbred population (96). In contrast, the mutations underlying complex diseases are likely to be common and much older (99,103), so LD with nearby markers will be much weaker. Secondly, it is unclear whether the bottlenecks experienced by most isolates are sufficient to effect a significant reduction in haplotype diversity within and near common disease genes (99,103). The relevant empirical data are as yet scant and fairly mixed: Some studies show substantial LD in defined isolates (118,132–134). Others reveal little difference in LD patterns or allelic diversity between outbred populations and those often considered to be isolates, such as Finland and Sardinia (81,129,140). [Part of the difficulty is in definitions: Finland, for example, is probably not an ‘isolate’ in toto but instead contains isolated subpopulations, although even these do not always have elevated LD (141).] Finally, there are clear disadvantages in using isolated populations, such as smaller sample sizes and lower levels of marker heterozygosity (101). Before dismissing or embracing population isolates as a resource for complex disease gene mapping, further studies of allelic and haplotypic diversity in these populations must be completed.
How much population stratification is there, and how should we deal with it?
It is well known that population stratification can affect the outcome of an association study (142–144). This concern led to the development of family-based association methods such as the transmission disequilibrium test (145,146). However, some investigators have questioned whether stratification is a significant problem (97,147), and they point out that family data are relatively difficult to collect and that family-based methods impose limitations on statistical power and efficiency. The results presented in Table 2 indicate substantial homogeneity among populations within each continent (especially Europe and Asia), suggesting that stratification may not be a major problem within continental populations. This conclusion is also suggested by the lack of stratification seen in numerous crime laboratory reference populations (6). In any case, stratification effects can be assessed and corrected using multiple unlinked markers (148).
Another way of exploring population stratification and allelic heterogeneity is to use the phylogenetic approaches commonly employed in evolutionary genetic analyses (99,130,144,149). Distinct clades of haplotypes, only some of which are associated with disease, have been revealed, for example, in analyses of the APOE (150) and DCP1 (ACE) (66) genes and in association studies of Y chromosome haplotypes and alcoholism susceptibility (151).
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONTHE DISTRIBUTION OF HUMAN...BIOMEDICAL APPLICATIONS OF HUMAN...CONCLUSIONSREFERENCES |
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It is heartening that the study of our basic DNA makeup can help to unify such seemingly disparate topics as evolutionary history and disease gene identification. Population genomics will help to resolve questions and controversies regarding the origins and affinities of our species. As we continue to learn more about this most fascinating of histories, we will also learn more about effective strategies for finding disease-causing genes.
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ACKNOWLEDGEMENTS |
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We are grateful for comments and discussion from Drs Henry Harpending and Alan Rogers. This work was supported by NIH grant GM-59290 and NSF grant SBR-9818215.
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FOOTNOTES |
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+ To whom correspondence should be addressed. Tel: +1 801 581 4566; Fax: +1 801 581 7796; Email: lbj@genetics.utah.edu
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G. T. Marth, E. Czabarka, J. Murvai, and S. T. Sherry The Allele Frequency Spectrum in Genome-Wide Human Variation Data Reveals Signals of Differential Demographic History in Three Large World Populations Genetics, January 1, 2004; 166(1): 351 - 372. [Abstract] [Full Text] [PDF]
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 U. Broeckel and N. J. Schork Identifying genes and genetic variation underlying human diseases and complex phenotypes via recombination mapping J. Physiol., January 1, 2004; 554(1): 40 - 45. [Abstract] [Full Text] [PDF]
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 E. Sanders-Bush, H. Fentress, and L. Hazelwood Serotonin 5-HT2 Receptors: Molecular and Genomic Diversity Mol. Interv., September 1, 2003; 3(6): 319 - 330. [Abstract] [Full Text] [PDF]
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 J. D.H. Stead, M. E. Hurles, and A. J. Jeffreys Global Haplotype Diversity in the Human Insulin Gene Region Genome Res., September 1, 2003; 13(9): 2101 - 2111. [Abstract] [Full Text] [PDF]
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 N. G. C. Smith, M. T. Webster, and H. Ellegren A Low Rate of Simultaneous Double-Nucleotide Mutations in Primates Mol. Biol. Evol., January 1, 2003; 20(1): 47 - 53. [Abstract] [Full Text] [PDF]
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 M. J. Bamshad, S. Mummidi, E. Gonzalez, S. S. Ahuja, D. M. Dunn, W. S. Watkins, S. Wooding, A. C. Stone, L. B. Jorde, R. B. Weiss, et al. A strong signature of balancing selection in the 5' cis-regulatory region of CCR5 PNAS, August 6, 2002; 99(16): 10539 - 10544. [Abstract] [Full Text] [PDF]
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