Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mapping by admixture linkage disequilibrium: advances, limitations and guidelines

Key Points

  • Mapping by admixture linkage disequilibrium (MALD) is a genetic strategy for discovering genes that underlie complex diseases. The method is based on differences in disease-gene frequency between the parental racial groups of admixed populations.

  • A MALD-based full-genome scan can be carried out using a few thousand markers that are able to differentiate, to a high degree, between chromosomal ancestries in relation to the parental popultions. This enables the discovery of regions that harbour genes associated with complex diseases.

  • Differences in the proportion of admixture for a particular chromosomal segment between cases and controls can implicate a region that is several centimorgans in size as being involved in a disease. This can also be done using cases only, by looking for differences in admixture proportions between specific locations and the rest of the genome in the same individual.

  • MALD-based genome scans are already possible in African-Americans, and are now underway. These studies are using a published set of MALD markers that are highly enriched for the ability to differentiate between chromosomal segments derived from African and European ancestors. The marker set will improve as frequency data accumulate from the HapMap project.

  • MALD scans in other groups (Latinos, Pacific Islanders and other admixed populations) will become possible in the near future as appropriate markers are mined from HapMap allele frequencies.

  • Proof of the efficacy of MALD awaits its successful application among African-Americans for potentially amenable diseases, such as prostate cancer, multiple sclerosis and end-stage renal disease. If these studies are successful, MALD could then be applied to other groups over the next few years.


Mapping by admixture linkage disequilibrium (MALD) is a theoretically powerful, although unproven, approach to mapping genetic variants that are involved in human disease. MALD takes advantage of long-range haplotypes that are generated by gene flow among recently admixed ethnic groups, such as African-Americans and Latinos. Under ideal circumstances, MALD will have more power to detect some genetic variants than other types of genome-wide association study that are carried out among more ethnically homogeneous populations. It will also require 200–500 times fewer markers, providing a significant economic advantage. The MALD approach is now being applied, with results expected in the near future.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Detecting disease-associated genomic regions using mapping by admixture linkage disequilibrium.
Figure 2: Assessment of linkage disequilibrium that is caused by admixture in African-Americans.
Figure 3: Ancestry-assessment estimates using the ANCESTRYMAP algorithm.


  1. 1

    Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    CAS  Article  Google Scholar 

  2. 2

    Gibbs, R. A. et al. The International HapMap Project. Nature 426, 789–796 (2003).

    CAS  Article  Google Scholar 

  3. 3

    Hirschhorn, J. N. & Daly, M. J. Genome-wide association studies for common diseases and complex traits. Nature Rev. Genet. 6, 95–108 (2005).

    CAS  Article  Google Scholar 

  4. 4

    Wang, W. Y., Barratt, B. J., Clayton, D. G. & Todd, J. A. Genome-wide association studies: theoretical and practical concerns. Nature Rev. Genet. 6, 109–118 (2005).

    CAS  Article  Google Scholar 

  5. 5

    Briscoe, D., Stephens, J. C. & O'Brien, S. J. Linkage disequilibrium in admixed populations: applications in gene mapping. J. Hered. 85, 59–63 (1994).

    CAS  PubMed  Google Scholar 

  6. 6

    Stephens, J. C., Briscoe, D. & O'Brien, S. J. Mapping by admixture linkage disequilibrium in human populations: limits and guidelines. Am. J. Hum. Genet. 55, 809–824 (1994). This is an early description of the power of MALD for identifying disease genes by exploring models of gene discovery. This paper describes the characteristics of suitable populations and the consequences of admixture for linkage disequilibrium.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    McKeigue, P. M. Mapping genes that underlie ethnic differences in disease risk: methods for detecting linkage in admixed populations, by conditioning on parental admixture. Am. J. Hum. Genet. 63, 241–251 (1998). This paper conceptualizes and describes the idea of gene mapping using admixture analysis based on chromosome segments that are derived from ancestral populations.

    CAS  Article  Google Scholar 

  8. 8

    Darvasi, A. & Shifman, S. The beauty of admixture. Nature Genet. 37, 118–119 (2005).

    CAS  Article  Google Scholar 

  9. 9

    Dean, M. et al. Polymorphic admixture typing in human ethnic populations. Am. J. Hum. Genet. 55, 788–808 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Smith, M. W. et al. Markers for mapping by admixture linkage disequilibrium in African American and Hispanic populations. Am. J. Hum. Genet. 69, 1080–1094 (2001).

    CAS  Article  Google Scholar 

  11. 11

    Smith, M. W. et al. A high-density admixture map for disease gene discovery in African Americans. Am. J. Hum. Genet. 74, 1001–1013 (2004). A genome-wide set of markers for MALD-based gene discovery in African-Americans is described in this paper. An average of 6 generations since admixture and average European chromosomal segments with block sizes of 11 cM are estimated.

    CAS  Article  Google Scholar 

  12. 12

    Montana, G. & Pritchard, J. K. Statistical tests for admixture mapping with case–control and cases-only data. Am. J. Hum. Genet. 75, 771–789 (2004). The authors describe the use of the program STRUCTURE and MALDsoft for admixture mapping with simulated data. They suggest that a large set of random SNPs can be used to discover disease genes nearly as well as a much smaller set of markers that are enriched for MALD-based gene discovery.

    CAS  Article  Google Scholar 

  13. 13

    Patterson, N. et al. Methods for high-density admixture mapping of disease genes. Am. J. Hum. Genet. 74, 979–1000 (2004). This paper describes a rapid gene-mapping algorithm (ANCESTRYMAP) for MALD that uses MCMC and hidden Markov chain methodologies that are capable of whole-genome admixture scans. It extensively models the sample sizes that are necessary for gene discovery using MALD.

    CAS  Article  Google Scholar 

  14. 14

    Seldin, M. F. et al. Putative ancestral origins of chromosomal segments in individual African Americans: implications for admixture mapping. Genome Res. 14, 1076–1084 (2004).

    CAS  Article  Google Scholar 

  15. 15

    Zhang, C., Chen, K., Seldin, M. F. & Li, H. A hidden Markov modeling approach for admixture mapping based on case–control data. Genet. Epidemiol. 27, 225–239 (2004).

    Article  Google Scholar 

  16. 16

    Zhu, X., Cooper, R. S. & Elston, R. C. Linkage analysis of a complex disease through use of admixed populations. Am. J. Hum. Genet. 74, 1136–1153 (2004).

    CAS  Article  Google Scholar 

  17. 17

    Chakraborty, R. & Weiss, K. M. Admixture as a tool for finding linked genes and detecting that difference from allelic association between loci. Proc. Natl Acad. Sci. USA 85, 9119–9123 (1988). A classic reference that describes admixture and its potential use in finding traits of interest.

    CAS  Article  Google Scholar 

  18. 18

    Hoggart, C. J. et al. Control of confounding of genetic associations in stratified populations. Am. J. Hum. Genet. 72, 1492–1504 (2003).

    CAS  Article  Google Scholar 

  19. 19

    Hoggart, C. J., Shriver, M. D., Kittles, R. A., Clayton, D. G. & McKeigue, P. M. Design and analysis of admixture mapping studies. Am. J. Hum. Genet. 74, 965–978 (2004). This article describes a methodology for admixture analysis and makes sample-size estimates for MALD-based gene discovery.

    CAS  Article  Google Scholar 

  20. 20

    Parra, E. J. et al. Estimating African American admixture proportions by use of population-specific alleles. Am. J. Hum. Genet. 63, 1839–1851 (1998).

    CAS  Article  Google Scholar 

  21. 21

    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data. Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Halder, I. & Shriver, M. D. Measuring and using admixture to study the genetics of complex disease. Hum. Genomics 1, 52–62 (2003).

    CAS  Article  Google Scholar 

  24. 24

    Wang, J. Maximum-likelihood estimation of admixture proportions from genetic data. Genetics 164, 747–765 (2003).

    PubMed  PubMed Central  Google Scholar 

  25. 25

    Reiner, A. P. et al. Population structure, admixture, and aging-related phenotypes in African American adults: the cardiovascular health study. Am. J. Hum. Genet. 76, 463–477 (2005).

    CAS  Article  Google Scholar 

  26. 26

    Madrigal, L. et al. Ethnicity, gene flow, and population subdivision in Limon, Costa Rica. Am. J. Phys. Anthropol. 114, 99–108 (2001).

    CAS  Article  Google Scholar 

  27. 27

    Bertoni, B., Budowle, B., Sans, M., Barton, S. A. & Chakraborty, R. Admixture in Hispanics: distribution of ancestral population contributions in the continental United States. Hum. Biol. 75, 1–11 (2003).

    Article  Google Scholar 

  28. 28

    Bonilla, C., Shriver, M. D., Parra, E. J., Jones, A. & Fernandez, J. R. Ancestral proportions and their association with skin pigmentation and bone mineral density in Puerto Rican women from New York city. Hum. Genet. (2004).

  29. 29

    Collins-Schramm, H. E. et al. Mexican American ancestry-informative markers: examination of population structure and marker characteristics in European Americans, Mexican Americans, Amerindians and Asians. Hum. Genet. 114, 263–271 (2004).

    Article  Google Scholar 

  30. 30

    Parra, E. J. et al. Relation of type 2 diabetes to individual admixture and candidate gene polymorphisms in the Hispanic American population of San Luis Valley, Colorado. J. Med. Genet. 41, e116 (2004).

    CAS  Article  Google Scholar 

  31. 31

    Rousham, E. K. & Gracey, M. Factors affecting birthweight of rural Australian Aborigines. Ann. Hum. Biol. 29, 363–372 (2002).

    CAS  Article  Google Scholar 

  32. 32

    Grandinetti, A. et al. Relationship between plasma glucose concentrations and Native Hawaiian ancestry: the Native Hawaiian Health Research Project. Int. J. Obes. Relat. Metab. Disord. 26, 778–782 (2002).

    CAS  Article  Google Scholar 

  33. 33

    Pfaff, C. L. et al. Population structure in admixed populations: effect of admixture dynamics on the pattern of linkage disequilibrium. Am. J. Hum. Genet. 68, 198–207 (2001).

    CAS  Article  Google Scholar 

  34. 34

    Lautenberger, J. A., Stephens, J. C., O'Brien, S. J. & Smith, M. W. Significant admixture linkage disequilibrium across 30 cM around the FY locus in African Americans. Am. J. Hum. Genet. 66, 969–978 (2000).

    CAS  Article  Google Scholar 

  35. 35

    Zhu, X. et al. Admixture mapping for hypertension loci with genome-scan markers. Nature Genet. 37, 177–181 (2005).

    CAS  Article  Google Scholar 

  36. 36

    Hinds, D. A. et al. Whole-genome patterns of common DNA variation in three human populations. Science 307, 1072–1079 (2005).

    CAS  Article  Google Scholar 

  37. 37

    McKeigue, P. M. Prospects for admixture mapping of complex traits. Am. J. Hum. Genet. 76, 1–7 (2004).

    Article  Google Scholar 

  38. 38

    Mountain, J. L. & Risch, N. Assessing genetic contributions to phenotypic differences among 'racial' and 'ethnic' groups. Nature Genet. 36, S48–S53 (2004).

    CAS  Article  Google Scholar 

  39. 39

    Schwartz, R. S. Racial profiling in medical research. N. Engl. J. Med. 344, 1392–1393 (2001).

    CAS  Article  Google Scholar 

  40. 40

    Braun, L. Race, ethnicity, and health: can genetics explain disparities? Perspect. Biol. Med. 45, 159–174 (2002).

    Article  Google Scholar 

  41. 41

    Pearce, N., Foliaki, S., Sporle, A. & Cunningham, C. Genetics, race, ethnicity, and health. BMJ 328, 1070–1072 (2004).

    Article  Google Scholar 

  42. 42

    Risch, N., Burchard, E., Ziv, E. & Tang, H. Categorization of humans in biomedical research: genes, race and disease. Genome Biol. 3, 1–12 (2002).

    Article  Google Scholar 

  43. 43

    Burchard, E. G. et al. The importance of race and ethnic background in biomedical research and clinical practice. N. Engl. J. Med. 348, 1170–1175 (2003).

    Article  Google Scholar 

  44. 44

    Bamshad, M., Wooding, S., Salisbury, B. A. & Stephens, J. C. Deconstructing the relationship between genetics and race. Nature Rev. Genet. 5, 598–609 (2004).

    CAS  Article  Google Scholar 

  45. 45

    Taylor, A. L. et al. Combination of isosorbide dinitrate and hydralazine in blacks with heart failure. N. Engl. J. Med. 351, 2049–2057 (2004).

    CAS  Article  Google Scholar 

  46. 46

    McEliece, R. The Theory of Information and Coding. Encyclopaedia of Math and its Applications Vol. 3 (Addison Wesley, 1977).

    Google Scholar 

  47. 47

    Reich, D. & Patterson, N. Pitfalls and prospects for admixture mapping. Philos. Trans. R. Soc. B (in the press).

  48. 48

    Thomas, D. L. et al. The natural history of hepatitis C virus infection: host, viral, and environmental factors. JAMA 284, 450–456 (2000).

    CAS  Article  Google Scholar 

  49. 49

    Tess, B. H., Rodrigues, L. C., Newell, M. L., Dunn, D. T. & Lago, T. D. Breastfeeding, genetic, obstetric and other risk factors associated with mother-to-child transmission of HIV-1 in Sao Paulo State, Brazil. Sao Paulo collaborative study for vertical transmission of HIV-1. Aids 12, 513–520 (1998).

    CAS  Article  Google Scholar 

  50. 50

    Hogancamp, W. E., Rodriguez, M. & Weinshenker, B. G. The epidemiology of multiple sclerosis. Mayo Clin. Proc. 72, 871–878 (1997).

    CAS  Article  Google Scholar 

  51. 51

    Ruo, B., Capra, A. M., Jensvold, N. G. & Go, A. S. Racial variation in the prevalence of atrial fibrillation among patients with heart failure: the Epidemiology, Practice, Outcomes, and Costs of Heart Failure (EPOCH) study. J. Am. Coll. Cardiol. 43, 429–435 (2004).

    Article  Google Scholar 

  52. 52

    Gupta, V. et al. Racial differences in thoracic aorta atherosclerosis among ischemic stroke patients. Stroke 34, 408–412 (2003).

    Article  Google Scholar 

  53. 53

    Bohannon, A. D. Osteoporosis and African American women. J. Womens Health Gend. Based Med. 8, 609–615 (1999).

    CAS  Article  Google Scholar 

  54. 54

    Finkelstein, J. S. et al. Ethnic variation in bone density in premenopausal and early perimenopausal women: effects of anthropometric and lifestyle factors. J. Clin. Endocrinol. Metab. 87, 3057–3067 (2002).

    CAS  Article  Google Scholar 

  55. 55

    Bastian, H. M. et al. Systemic lupus erythematosus in three ethnic groups. XII. Risk factors for lupus nephritis after diagnosis. Lupus 11, 152–160 (2002).

    CAS  Article  Google Scholar 

  56. 56

    Davey Smith, G., Neaton, J. D., Wentworth, D., Stamler, R. & Stamler, J. Mortality differences between black and white men in the USA: contribution of income and other risk factors among men screened for the MRFIT. Lancet 351, 934–939 (1998).

    CAS  Article  Google Scholar 

  57. 57

    Demirovic, J. et al. Prevalence of dementia in three ethnic groups: the South Florida program on aging and health. Ann. Epidemiol. 13, 472–478 (2003).

    Article  Google Scholar 

  58. 58

    Harper, M. A. et al. Racial disparity in pregnancy-related mortality following a live birth outcome. Ann. Epidemiol. 14, 274–279 (2004).

    Article  Google Scholar 

  59. 59

    Kopp, J. B. & Winkler, C. HIV-associated nephropathy in African Americans. Kidney Int. S43–S49 (2003).

  60. 60

    Songer, T. J. & Zimmet, P. Z. Epidemiology of type II diabetes: an international perspective. Pharmacoeconomics 8 (Suppl. 1), 1–11 (1995).

    Article  Google Scholar 

  61. 61

    Klag, M. J. et al. End-stage renal disease in African-American and white men. 16-year MRFIT findings. JAMA 277, 1293–1298 (1997).

    CAS  Article  Google Scholar 

  62. 62

    Kissela, B. et al. Stroke in a biracial population: the excess burden of stroke among blacks. Stroke 35, 426–431 (2004).

    Article  Google Scholar 

  63. 63

    Wong, T. Y. et al. Racial differences in the prevalence of hypertensive retinopathy. Hypertension 41, 1086–1091 (2003).

    CAS  Article  Google Scholar 

  64. 64

    McGinnis, K. A. et al. Understanding racial disparities in HIV using data from the veterans aging cohort 3-site study and VA administrative data. Am. J. Public Health 93, 1728–1733 (2003).

    Article  Google Scholar 

  65. 65

    Hodge, A. M. & Zimmet, P. Z. The epidemiology of obesity. Baillieres Clin. Endocrinol. Metab. 8, 577–599 (1994).

    CAS  Article  Google Scholar 

  66. 66

    Molokhia, M. & McKeigue, P. Risk for rheumatic disease in relation to ethnicity and admixture. Arthritis Res. 2, 115–125 (2000).

    CAS  Article  Google Scholar 

  67. 67

    Reveille, J. D. Ethnicity and race and systemic sclerosis: how it affects susceptibility, severity, antibody genetics, and clinical manifestations. Curr. Rheumatol. Rep. 5, 160–167 (2003).

    Article  Google Scholar 

  68. 68

    Wright, N. M., Papadea, N., Veldhuis, J. D. & Bell, N. H. Growth hormone secretion and bone mineral density in prepubertal black and white boys. Calcif. Tissue Int. 70, 146–152 (2002).

    CAS  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Michael W. Smith.

Supplementary information

Related links

Related links






ADMIXMAP software


Family Linkage Mapping resources at the Marshfield Center for Medical Genetics

International HapMap Project

MALDsoft software



A method for localizing genes that is based on the co-inheritance of genetic markers and phenotypes in families over several generations.


A gene-discovery strategy that compares cases with controls to assess the contribution of genetic variants to phenotypes in specific populations.


The sequence of a single chromosome, summarized as a unique combination of known polymorphic sites.


The formation of a new population by interbreeding between individuals from genetically divergent parental populations, and subsequently by interbreeding between their offspring.


The non-random association of genetic variants due to admixture that decays rapidly (in a few generations) between unlinked genes and more slowly between linked ones.


Fixation occurs when a specific allele at a locus is found exclusively in one population but in another, an alternative allele is exclusively present.


A chi-squared analysis of the numbers of observations to test for differences between categories in a data table.


A statistical estimation technique that estimates parameters on the basis of minimizing the square of the differences between a model and the observations.


A method for estimating parameter values in a model that have the highest probability of explaining the data observed.


A statistical methodology that takes prior knowledge into account.


The process by which individuals in a population choose each other as mates with equal likelihood.


Encoded by the FY gene, this is an antigen expressed on red blood cells that is a scavenger receptor for chemokines and also serves as a receptor for the malarial parasite, Plasmodium vivax.


A measure that is used to quantify the informativeness of a marker or set of markers for determining the ancestral state of a chromosomal segment or locus.


(HMM). A statistical model of a sequence of events for which the probability of an event occurring depends on previous and subsequent events occurring. It is useful in admixture mapping as a complex and interdependent model can be calculated to fit the segmental nature of admixed chromosomes.


(MCMC). The distributions underlying the hidden Markov model are extremely complex, making their direct estimation a huge task. This is simplified in MCMC analysis by generating averages of the expectations from the underlying distributions to model and analyse the results of admixture mapping.


Statistical tests which use models that make assumptions about the distributions of sample values and parameters.


Statistical procedures that are not based on models or assumptions pertaining to the distribution of the variable.


An approach in which the actual data are randomized many times to generate a distribution of outcomes, so that the fraction of observations with values that are more extreme than the outcome that is observed with the real data reflects the statistical significance.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Smith, M., O'Brien, S. Mapping by admixture linkage disequilibrium: advances, limitations and guidelines. Nat Rev Genet 6, 623–632 (2005).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing