Linkage disequilibrium — understanding the evolutionary past and mapping the medical future

Key Points

  • Linkage disequilibrium (LD) is the nonrandom association of alleles of different loci. There is no single best statistic that quantifies the extent of LD. Several statistics have been proposed that are useful for different purposes.

  • Recombination interacts in a complex way with selection, mutation and genetic drift to determine levels of LD. As a consequence, local and genome-wide patterns of LD can provide insight into patterns of natural selection and the past history of population growth and dispersal.

  • In humans and other model organisms, LD between marker alleles and traits of interest allow fine-scale gene mapping. Many recent genome-wide association studies have successfully mapped SNPs associated with complex inherited diseases in humans.

  • Unusually high local LD can indicate an allele that has recently increased to high frequency under strong selection. Several methods have been developed to detect selected loci and to estimate the age of alleles using patterns of LD.

  • In humans, the analysis of LD is well underway. The pace is slower in other species, although some model organisms, including mice, dogs, Drosophila and Arabidopsis thaliana, are catching up fast. Extensive analysis of LD in non-model species will be undertaken soon.

Abstract

Linkage disequilibrium — the nonrandom association of alleles at different loci — is a sensitive indicator of the population genetic forces that structure a genome. Because of the explosive growth of methods for assessing genetic variation at a fine scale, evolutionary biologists and human geneticists are increasingly exploiting linkage disequilibrium in order to understand past evolutionary and demographic events, to map genes that are associated with quantitative characters and inherited diseases, and to understand the joint evolution of linked sets of genes. This article introduces linkage disequilibrium, reviews the population genetic processes that affect it and describes some of its uses. At present, linkage disequilibrium is used much more extensively in the study of humans than in non-humans, but that is changing as technological advances make extensive genomic studies feasible in other species.

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Figure 1: Haplotype blocks.

References

  1. 1

    Lewontin, R. C. & Kojima, K. The evolutionary dynamics of complex polymorphisms. Evolution 14, 458–472 (1960).

    Google Scholar 

  2. 2

    Weir, B. S. Genetic Data Analysis II (Sinauer Assoc., Sunderland, Massachusetts, 1996).

    Google Scholar 

  3. 3

    Hedrick, P. W. Genetic disequilibrium measures: proceed with caution. Genetics 117, 331–341 (1987). This paper and the reply by Lewontin (reference 33) point out many of the logical and statistical difficulties in attempting to define a 'best' LD statistic.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Abecasis, G. R. & Cookson, W. O. C. GOLD — Graphical Overview of Linkage Disequilibrium. Bioinformatics 16, 182–183 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5

    Zhao, H., Nettleton, D. & Dekkers, J. C. M. Evaluation of linkage disequilibrium measures between multi-allelic markers as predictors of linkage disequilibrium between single nucleotide polymorphisms. Genet. Res. 89, 1–6 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6

    Meyer, D., Single, R. M., Mack, S. J., Erlich, H. A. & Thomson, G. Signatures of demographic history and natural selection in the human major histocompatibility complex loci. Genetics 173, 2121–2142 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7

    Weinberg, W. Uber vererbungsgesetze beim menschen. Z. Abst V. Vererb. 1, 276–330 (1909).

    Google Scholar 

  8. 8

    Jennings, H. S. The numerical results of diverse systems of breeding, with respect to two pairs of characters, linked and independent, with special relation to the effects of linkage. Genetics 2, 97–154 (1917).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Weir, B. S. Inferences about linkage disequilibrium. Biometrics 35, 235–254 (1979).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10

    Excoffier, L. & Slatkin, M. Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol. Biol. Evol. 12, 921–927 (1995).

    CAS  PubMed  Google Scholar 

  11. 11

    Kuhner, M. K. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22, 768–770 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12

    Miyashita, N. & Langley, C. H. Molecular and phenotypic variation of the white locus region in Drosophila melanogaster. Genetics 120, 199–212 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Daly, M. J., Rioux, J. D., Schaffner, S. F., Hudson, T. J. & Lander, E. S. High-resolution haplotype structure in the human genome. Nature 29, 229–232 (2001). This paper presents the first clear evidence of haplotype blocks in the human genome and the first method for detecting block boundaries.

    CAS  Google Scholar 

  14. 14

    Gabriel, S. B. et al. The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002).

    CAS  Article  Google Scholar 

  15. 15

    Wall, J. D. & Pritchard, J. K. Haplotype blocks and linkage disequilibrium in the human genome. Nature Rev. Genet. 4, 587–597 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Kruglyak, L. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nature Genet. 22, 139–144 (1999).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17

    Carlson, C. S. et al. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am. J. Hum. Genet. 74, 106–120 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Phillips, M. S. et al. Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots. Nature Genet. 33, 382–387 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Anderson, E. C. & Novembre, J. Finding haplotype block boundaries by using the minimum-description-length principle. Am. J. Hum. Genet. 73, 336–354 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20

    International HapMap Consortium. A haplotype map of the human genome. Nature 437, 1299–1320 (2005).

  21. 21

    International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  22. 22

    Guryev, V. et al. Haplotype block structure is conserved across mammals. PLoS Genet. 2, 1111–1118 (2006).

    CAS  Article  Google Scholar 

  23. 23

    Gautier, M. et al. Genetic and haplotypic structure in 14 European and African cattle breeds. Genetics 177, 1059–1070 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24

    Lindblad-Toh, K. et al. Genome sequence, comparative analysis and haplotype structure of the domestic dog. Nature 438, 803–819 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25

    Brown, A. H. D., Feldman, M. W. & Nevo, E. Multilocus structure of natural populations of Hordeum spontaneum. Genetics 96, 523–536 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Maynard Smith, J., Smith, N. H., O'Rourke, M. & Spratt, B. G. How clonal are bacteria? Proc. Natl Acad. Sci. USA 90, 4384–4388 (1993).

    Article  Google Scholar 

  27. 27

    Geiringer, H. On the probability theory of linkage in Mendelian heredity. Annals of Mathematical Statistics 15, 25–57 (1944).

    Article  Google Scholar 

  28. 28

    Grote, M. N., Klitz, W. & Thomson, G. Constrained disequilibrium values and hitchhiking in a three-locus system. Genetics 150, 1295–1307 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Ohta, T. Linkage disequilibrium due to random genetic drift in finite subdivided populations. Proc. Natl. Acad. Sci. USA 79, 1940–1944 (1982).

    CAS  Article  Google Scholar 

  30. 30

    Ohta, T. Linkage disequilibrium with the island model. Genetics 101, 139–155 (1982).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Wright, S. Breeding structure of populations in relation to speciation. Am. Nat. 74, 232–248 (1940).

    Article  Google Scholar 

  32. 32

    Raymond, M. & Rousset, F. Genepop (Version 1.2) — population-genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).

    Article  Google Scholar 

  33. 33

    Lewontin, R. C. On measures of gametic disequilibrium. Genetics 120, 849–852 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Maniatis, N., Morton, N. E., Xu, C. F., Hosking, L. K. & Collins, A. The optimal measure of linkage disequilibrium reduces error in association mapping of affection status. Hum. Mol. Genet. 14, 145–153 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35

    Hudson, R. R. Properties of a neutral allele model with intragenic recombination. Theor. Popul. Biol. 23, 183–201 (1983). This paper presents the first coalescent model with recombination.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36

    Hudson, R. R. & Kaplan, N. L. Statistical properties of the number of recombination events in the history of a sample of DNA sequences. Genetics 111, 147–164 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Kuhner, M. K., Yamato, J. & Felsenstein, J. Maximum likelihood estimation of recombination rates from population data. Genetics 156, 1393–1401 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Hudson, R. R. Two-locus sampling distributions and their applications. Genetics 159, 1805–1817 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    McVean, G., Awadalla, P. & Fearnhead, P. A coalescent-based method for detecting and estimating recombination from gene sequences. Genetics 160, 1231–1241 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Myers, S., Bottolo, L., Freeman, C., McVean, G. & Donnelly, P. A fine-scale map of recombination rates and hotspots across the human genome. Science 310, 321–324 (2005). This paper applies the method described in reference 39 to human HapMap data and demonstrates the ubiquity of recombinational hot spots and identifies a DNA sequence motif that is associated with elevated recombination rates.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. 41

    Kimura, M. Attainment of quasi linkage equilibrium when gene frequencies are changing by natural selection. Genetics 52, 875–890 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Nagylaki, T. Quasilinkage equilibrium and the evolution of two-locus systems. Proc. Natl. Acad. Sci. USA 71, 526–530 (1974).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43

    Nagylaki, T. The evolution of one and two-locus systems. Genetics 83, 583–600 (1976).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Fisher, R. A. The Genetical Theory of Natural Selection (Clarendon, Oxford, 1930).

    Google Scholar 

  45. 45

    Felsenstein, J. The effect of linkage on directional selection. Genetics 52, 349–363 (1965).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Karlin, S. & Feldman, M. W. Linkage and selection: two locus symmetric viability model. Theor. Popul. Biol. 1, 39–71 (1970).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. 47

    Feldman, M. W., Franklin, I. & Thomson, G. J. Selection in complex genetic systems I. The symmetric equilibria of the three-locus symmetric viability model. Genetics 76, 135–162 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Franklin, I. & Lewontin, R. C. Is the gene the unit of selection? Genetics 65, 707–734 (1970).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Slatkin, M. On treating the chromosome as the unit of selection. Genetics 72, 157–168 (1972).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Charlesworth, B. & Charlesworth, D. Study of linkage disequilibrium in populations of Drosophila melanogaster. Genetics 73, 351–359 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Langley, C. H., Tobari, Y. N. & Kojima, K. I. Linkage disequilibrium in natural populations of Drosophila melanogaster. Genetics 78, 921–936 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Hamon, S. C. et al. Evidence for consistent intragenic and intergenic interactions between SNP effects in the APOA1/C3/A4/A5 gene cluster. Hum. Hered. 61, 87–96 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53

    Hill, W. G. & Robertson, A. Linkage disequilibrium in finite populations. Theor. Appl. Genet. 38, 226–231 (1968).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54

    Ohta, T. & Kimura, M. Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics 63, 229–238 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Hudson, R. R. The sampling distribution of linkage disequilibrium under an infinite allele model without selection. Genetics 109, 611–631 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56

    Slatkin, M. Linkage disequilibrium in growing and stable populations. Genetics 137, 331–336 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Hill, W. G. & Robertson, A. The effect of linkage on limits to artificial selection. Genet. Res. 8, 269–294 (1966).

    CAS  Article  Google Scholar 

  58. 58

    McVean, G. A. T. & Charlesworth, B. The effects of Hill–Robertson interference between weakly selected mutations on patterns of molecular evolution and variation. Genetics 155, 929–944 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Felsenstein, J. The evolutionary advantage of recombination. Genetics 78, 737–756 (1974). This is the first paper to recognize the Hill–Robertson effect and its implications for the evolution of sex and recombination.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Keightley, P. D. & Otto, S. P. Interference among deleterious mutations favours sex and recombination in finite populations. Nature 443, 89–92 (2006).

    CAS  Article  Google Scholar 

  61. 61

    Barton, N. H. A general model for the evolution of recombination. Genet. Res. 65, 123–144 (1995).

    CAS  Article  Google Scholar 

  62. 62

    Nei, M. & Li, W. Linkage disequilibrium in subdivided populations. Genetics 75, 213–219 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Mitton, J. B., Koehn, R. K. & Prout, T. Population genetics of marine pelecypods. III. Epistasis between functionally related isoenzymes of Mytilus edulis. Genetics 73, 487–496 (1973).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Li, W. H. Stable linkage disequilibrium without epistasis in subdivided populations. Theor. Popul. Biol. 6, 173–183 (1974).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. 65

    Slatkin, M. Gene flow and selection in a 2-locus system. Genetics 81, 787–802 (1975).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Noonan, J. P. et al. Sequencing and analysis of Neanderthal genomic DNA. Science 314, 1113–1118 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67

    Schmegner, C., Hoegel, J., Vogel, W. & Assum, G. Genetic variability in a genomic region with long-range linkage disequilibrium reveals traces of a bottleneck in the history of the European population. Hum. Genet. 118, 276–286 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68

    Zhang, W. H. et al. Impact of population structure, effective bottleneck time, and allele frequency on linkage disequilibrium maps. Proc. Natl. Acad. Sci. USA 101, 18075–18080 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. 69

    Thornton, K. & Andolfatto, P. Approximate Bayesian inference reveals evidence for a recent, severe bottleneck in a Netherlands population of Drosophila melanogaster. Genetics 172, 1607–1619 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 70

    Weir, B. S. & Cockerham, C. C. Group inbreeding with 2 linked loci. Genetics 63, 711–742 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Golding, G. B. & Strobeck, C. Linkage disequilibrium in a finite population that is partially selfing. Genetics 94, 777–789 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Nordborg, M. et al. The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol. 3, 1289–1299 (2005).

    CAS  Article  Google Scholar 

  73. 73

    Kim, S. et al. Recombination and linkage disequilibrium in Arabidopsis thaliana. Nature Genet. 39, 1151–1155 (2007).

    CAS  Article  Google Scholar 

  74. 74

    Wiehe, T., Mountain, J., Parham, P. & Slatkin, M. Distinguishing recombination and intragenic gene conversion by linkage disequilibrium patterns. Genet. Res. 75, 61–73 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75

    Ardlie, K. et al. Lower-than-expected linkage disequilibrium between tightly linked markers in humans suggests a role for gene conversion. Am. J. Hum. Genet. 69, 582–589 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76

    Padhukasahasram, B., Marjoram, P. & Nordborg, M. Estimating the rate of gene conversion on human chromosome 21. Am. J. Hum. Genet. 75, 386–397 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. 77

    Gay, J., Myers, S. & McVean, G. Estimating meiotic gene conversion rates from population genetic data. Genetics 177, 881–894 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78

    Frisse, L. et al. Gene conversion and different population histories may explain the contrast between polymorphism and linkage disequilibrium levels. Am. J. Hum. Genet. 69, 831–843 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  79. 79

    Easton, D. F. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447, 1087–1093 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  80. 80

    Stacey, S. N. et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nature Genet. 39, 865–869 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  81. 81

    Tomlinson, I. et al. A genome-wide association scan of tag SNPs identifies a susceptibility variant for colorectal cancer at 8q24.21. Nature Genet. 39, 984–988 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  82. 82

    Zanke, B. W. et al. Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nature Genet. 39, 989–994 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. 83

    Diabetes Genetics Initiative, Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).

  84. 84

    Zeggini, E. et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316, 1336–1341 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. 85

    Scott, L. J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  86. 86

    Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

    CAS  Article  Google Scholar 

  87. 87

    Gudbjartsson, D. F. et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature 448, 353–357 (2007).

    CAS  Article  PubMed  Google Scholar 

  88. 88

    McPherson, R. et al. A common allele on chromosome 9 associated with coronary heart disease. Science 316, 1488–1491 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  89. 89

    Kohler, K. & Bickeboller, H. Case–control association tests correcting for population stratification. Ann. Hum. Genet. 70, 98–115 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  90. 90

    Pritchard, J. K. & Donnelly, P. Case–control studies of association in structured or admixed populations. Theor. Popul. Biol. 60, 227–237 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  91. 91

    Maynard Smith, J. & Haigh, J. The hitch-hiking effect of a favourable gene. Genet. Res. 23, 23–35 (1974).

    Article  Google Scholar 

  92. 92

    Kim, Y. & Stephan, W. Detecting a local signature of genetic hitchhiking along a recombining chromosome. Genetics 160, 765–777 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93

    Przeworski, M. Estimating the time since the fixation of a beneficial allele. Genetics 164, 1667–1676 (2003).

    PubMed  PubMed Central  Google Scholar 

  94. 94

    Nielsen, R. et al. Genomic scans for selective sweeps using SNP data. Genome Res. 15, 1566–1575 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  95. 95

    Stephens, J. C. et al. Dating the origin of the CCR5–Delta32 AIDS-resistance allele by the coalescence of haplotypes. Am. J. Hum. Genet. 62, 1507–1515 (1998).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  96. 96

    Slatkin, M. & Bertorelle, G. The use of intra-allelic variability for testing neutrality and estimating population growth rate. Genetics 158, 865–874 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Hudson, R. R., Bailey, K., Skarecky, D., Kwiatowski, J. & Ayala, F. J. Evidence for positive selection in the superoxide dismutase (Sod) region of Drosophila melanogaster. Genetics 136, 1329–1340 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98

    Depaulis, F. & Veuille, M. Neutrality tests based on the distribution of haplotypes under an infinite-site model. Mol. Biol. Evol. 15, 1788–1790 (1998).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  99. 99

    Sabeti, P. C. et al. Detecting recent positive selection in the human genome from haplotype structure. Nature 419, 832–837 (2002).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  100. 100

    Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007). This paper and reference 101 are among the first to show the feasibility of testing for selection on a genome-wide scale.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  101. 101

    Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, 446–458 (2006).

    CAS  Article  Google Scholar 

  102. 102

    Reich, D. E. & Goldstein, D. B. in Microsatellies: Evolution and Applications (eds Goldstein, D. B. & Schlötterer, C.) 129–138 (Oxford University Press, Oxford, 1999).

    Google Scholar 

  103. 103

    Kaplan, N. L., Lewis, P. O. & Weir, B. S. Age of the ΔF508 cystic fibrosis mutation. Nature Genet. 8, 216 (1994).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  104. 104

    Slatkin, M. & Rannala, B. Estimating the age of alleles by use of intraallelic variability. Am. J. Hum. Genet. 60, 447–458 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105

    Guo, S. W. & Xiong, M. Estimating the age of mutant disease alleles based on linkage disequilibrium. Hum. Hered. 47, 315–337 (1997).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  106. 106

    Slatkin, M. A Bayesian method for jointly estimating allele age and selection intensity. Genet. Res. 90, 119–128 (2008).

    Article  CAS  Google Scholar 

  107. 107

    Kaiser, J. DNA sequencing: A plan to capture human diversity in 1000 Genomes. Science 319, 395 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  108. 108

    Barker, J. The human genome diversity project — 'Peoples', 'populations' and the cultural politics of identification. Cultural Studies 18, 571–606 (2004).

    Article  Google Scholar 

  109. 109

    Cunningham, H. Colonial encounters in postcolonial contexts — patenting indigenous DNA and the Human Genome Diversity Project. Crit. Anthropol. 18, 205–233 (1998).

    Article  Google Scholar 

  110. 110

    Kahn, P. Genetic diversity project tries again. Science 266, 720–722 (1994).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  111. 111

    Wall, J. D. Detecting ancient admixture in humans using sequence polymorphism data. Genetics 154, 1271–1279 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112

    Plagnol, V. & Wall, J. D. Possible ancestral structure in human populations. Plos Genet. 2, 972–979 (2006).

    CAS  Article  Google Scholar 

  113. 113

    Evans, P. D., Mekel-Bobrov, N., Vallender, E. J., Hudson, R. R. & Lahn, B. T. Evidence that the adaptive allele of the brain size gene microcephalin introgressed into Homo sapiens from an archaic Homo lineage. Proc. Natl. Acad. Sci. USA 103, 18178–18183 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  114. 114

    Begun, D. J. et al. Population genomics: whole-genome analysis of polymorphism and divergence in Drosophila simulans. PLoS Biol. 5, e310 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Tishkoff, S. A. et al. Global patterns of linkage disequilibrium at the CD4 locus and modern human origins. Science 271, 1380–1387 (1996).

    CAS  Article  Google Scholar 

  116. 116

    Mountain, J. L. et al. SNPSTRs: empirically derived, rapidly typed, autosomal haplotypes for inference of population history and mutational processes. Genome Res. 12, 1766–1772 (2002).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  117. 117

    Tuzun, E. et al. Fine-scale structural variation of the human genome. Nature Genet. 37, 727–732 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  118. 118

    Conrad, D. F., Andrews, T. D., Carter, N. P., Hurles, M. E. & Pritchard, J. K. A high-resolution survey of deletion polymorphism in the human genome. Nature Genet. 38, 75–81 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  119. 119

    Lewontin, R. C. The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49, 49–67 (1964).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Bengtsson, B. O. & Thomson, G. Measuring the strength of associations between HLA antigens and diseases. Tissue Antigens 18, 356–363 (1981).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  121. 121

    Clark, A. G. et al. Haplotype structure and population genetic inferences from nucleotide-sequence variation in human lipoprotein lipase. Am. J. Hum. Genet. 63, 595–612 (1998).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  122. 122

    Hill, W. G. Estimation of linkage disequilibrium in randomly mating populations. Heredity 33, 229–239 (1974).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  123. 123

    Clark, A. G. Inference of haplotypes from PCR-amplified samples of diploid populations. Mol. Biol. Evol. 7, 111–122 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124

    Eskin, E., Halperin, E. & Karp, R. M. Efficient reconstruction of haplotype structure via perfect phylogeny. J. Bioinform. Comput. Biol. 1, 1–20 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  125. 125

    Stephens, M., Smith, N. J. & Donnelly, P. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet. 68, 978–989 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  126. 126

    Marchini, J. et al. A comparison of phasing algorithms for trios and unrelated individuals. Am. J. Hum. Genet. 78, 437–450 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  127. 127

    Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature Genet. 39, 906–913 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  128. 128

    Hästbacka, J. et al. Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nature Genet. 2, 204–211 (1992).

    Article  PubMed  PubMed Central  Google Scholar 

  129. 129

    Hästbacka, J. et al. The diastrophic dysplasia gene encodes a novel sulfate transporter — positional cloning by fine-structure linkage disequilibrium mapping. Cell 78, 1073–1087 (1994).

    Article  PubMed  PubMed Central  Google Scholar 

  130. 130

    Jeffreys, A. J., Kauppi, L. & Neumann, R. Intensely punctate meiotic recombination in the class II region of the major histocompatibility complex. Nature Genet. 29, 217–222 (2001). This paper presents the first experimental demonstration of hot spots of recombination along with evidence of their association with haplotype blocks.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The writing of this paper was supported in part by a grant from the US National Institutes of Health, R01-GM40282. I thank J. Felsenstein for discussions of this topic and the translation of Weinberg's paper, and M. Kirkpatrick and the referees for comments on an earlier version of this paper.

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Montgomery Slatkin's Research Group

Genepop

1000 Genomes Project

International HapMap Project

Likelihood analysis with metropolis algorithm using random coalescence (LAMARC)

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Slatkin, M. Linkage disequilibrium — understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9, 477–485 (2008). https://doi.org/10.1038/nrg2361

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