Linkage disequilibrium

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In population genetics, linkage disequilibrium (LD) is the non-random association of alleles at different loci in a given population. Loci are said to be in linkage disequilibrium when the frequency of association of their different alleles is higher or lower than what would be expected if the loci were independent and associated randomly.[1]

Linkage disequilibrium is influenced by many factors, including selection, the rate of genetic recombination, mutation rate, genetic drift, the system of mating, population structure, and genetic linkage. As a result, the pattern of linkage disequilibrium in a genome is a powerful signal of the population genetic processes that are structuring it.

In spite of its name, linkage disequilibrium may exist between alleles at different loci without any genetic linkage between them and independently of whether or not allele frequencies are in equilibrium (not changing with time).[1] Furthermore, linkage disequilibrium is sometimes referred to as gametic phase disequilibrium;[2] however, the concept also applies to asexual organisms and therefore does not depend on the presence of gametes.

Formal definition[edit]

Suppose that among the gametes that are formed in a sexually reproducing population, allele A occurs with frequency at one locus (i.e. is the proportion of gametes with A at that locus), while at a different locus allele B occurs with frequency . Similarly, let be the frequency with which both A and B occur together in the same gamete (i.e. is the frequency of the AB haplotype).

The association between the alleles A and B can be regarded as completely random—which is known in statistics as independence—when the occurrence of one does not affect the occurrence of the other, in which case the probability that both A and B occur together is given by the product of the probabilities. There is said to be a linkage disequilibrium between the two alleles whenever differs from for any reason.

The level of linkage disequilibrium between A and B can be quantified by the coefficient of linkage disequilibrium , which is defined as

provided that both and are greater than zero. Linkage disequilibrium corresponds to . In the case we have and the alleles A and B are said to be in linkage equilibrium. The subscript "AB" on emphasizes that linkage disequilibrium is a property of the pair {A, B} of alleles and not of their respective loci. Other pairs of alleles at those same two loci may have different coefficients of linkage disequilibrium.

For two biallelic loci, where a and b are the other alleles at these two loci, the restrictions are so strong that only one value of D is sufficient to represent all linkage disequilibrium relationships between these alleles. In this case, . Their relationships can be characterized as follows.[3]

The sign of D in this case is chosen arbitrarily. The magnitude of D is more important than the sign of D because the magnitude of D is representative of the degree of linkage disequilibrium.[4] However, positive D value means that the gamete is more frequent than expected while negative means that the combination of these two alleles are less frequent than expected.

Linkage disequilibrium in asexual populations can be defined in a similar way in terms of population allele frequencies. Furthermore, it is also possible to define linkage disequilibrium among three or more alleles, however these higher-order associations are not commonly used in practice.[1]

Measures derived from D[edit]

The coefficient of linkage disequilibrium is not always a convenient measure of linkage disequilibrium because its range of possible values depends on the frequencies of the alleles it refers to. This makes it difficult to compare the level of linkage disequilibrium between different pairs of alleles.

Lewontin[5] suggested normalising D by dividing it by the theoretical maximum difference between the observed and expected haplotype frequencies as follows:


An alternative to is the correlation coefficient between pairs of loci, usually expressed as its square, [6]

Limits for the ranges of linkage disequilibrium measures[edit]

The measures and have limits to their ranges and do not range over all values of zero to one for all pairs of loci. The maximum of depends on the allele frequencies at the two loci being compared and can only range fully from zero to one where either the allele frequencies at both loci are equal, where , or when the allele frequencies have the relationship when .[7] While can always take a maximum value of 1, its minimum value for two loci is equal to for those loci.[8]

Example: Two-loci and two-alleles[edit]

Consider the haplotypes for two loci A and B with two alleles each—a two-loci, two-allele model. Then the following table defines the frequencies of each combination:

Haplotype Frequency

Note that these are relative frequencies. One can use the above frequencies to determine the frequency of each of the alleles:

Allele Frequency

If the two loci and the alleles are independent from each other, then one can express the observation as " is found and is found". The table above lists the frequencies for , , and for, , hence the frequency of is , and according to the rules of elementary statistics .

The deviation of the observed frequency of a haplotype from the expected is a quantity[9] called the linkage disequilibrium[10] and is commonly denoted by a capital D:

The following table illustrates the relationship between the haplotype frequencies and allele frequencies and D.


Role of recombination[edit]

In the absence of evolutionary forces other than random mating, Mendelian segregation, random chromosomal assortment, and chromosomal crossover (i.e. in the absence of natural selection, inbreeding, and genetic drift), the linkage disequilibrium measure converges to zero along the time axis at a rate depending on the magnitude of the recombination rate between the two loci.

Using the notation above, , we can demonstrate this convergence to zero as follows. In the next generation, , the frequency of the haplotype , becomes

This follows because a fraction of the haplotypes in the offspring have not recombined, and are thus copies of a random haplotype in their parents. A fraction of those are . A fraction have recombined these two loci. If the parents result from random mating, the probability of the copy at locus having allele is and the probability of the copy at locus having allele is , and as these copies are initially in the two different gametes that formed the diploid genotype, these are independent events so that the probabilities can be multiplied.

This formula can be rewritten as

so that

where at the -th generation is designated as . Thus we have

If , then so that converges to zero.

If at some time we observe linkage disequilibrium, it will disappear in the future due to recombination. However, the smaller the distance between the two loci, the smaller will be the rate of convergence of to zero.

Example: Human leukocyte antigen (HLA) alleles[edit]

HLA constitutes a group of cell surface antigens also known as the MHC of humans. Because HLA genes are located at adjacent loci on the particular region of a chromosome and presumed to exhibit epistasis with each other or with other genes, a sizable fraction of alleles are in linkage disequilibrium.

An example of such linkage disequilibrium is between HLA-A1 and B8 alleles in unrelated Danes[11] referred to by Vogel and Motulsky (1997).[12]

Table 1. Association of HLA-A1 and B8 in unrelated Danes[11]
Antigen j Total
Antigen i
No. of individuals

Because HLA is codominant and HLA expression is only tested locus by locus in surveys, LD measure is to be estimated from such a 2×2 table to the right.[12][13][14][15]

expression () frequency of antigen  :

expression () frequency of antigen  :

frequency of gene , given that individuals with '+/−', '+/+', and '−/+' genotypes are all positive for antigen :


Denoting the '―' alleles at antigen i to be x, and at antigen j to be y, the observed frequency of haplotype xy is

and the estimated frequency of haplotype xy is

Then LD measure is expressed as

Standard errors are obtained as follows:

Then, if

exceeds 2 in its absolute value, the magnitude of is statistically significantly large. For data in Table 1 it is 20.9, thus existence of statistically significant LD between A1 and B8 in the population is admitted.

Table 2. Linkage disequilibrium among HLA alleles in pan-Europeans[15]
HLA-A alleles i HLA-B alleles j
A1 B8 0.065 16.0
A3 B7 0.039 10.3
A2 Bw40 0.013 4.4
A2 Bw15 0.01 3.4
A1 Bw17 0.014 5.4
A2 B18 0.006 2.2
A2 Bw35 −0.009 −2.3
A29 B12 0.013 6.0
A10 Bw16 0.013 5.9

Table 2 shows some of the combinations of HLA-A and B alleles where significant LD was observed among pan-Europeans.[15]

Vogel and Motulsky (1997)[12] argued how long would it take that linkage disequilibrium between loci of HLA-A and B disappeared. Recombination between loci of HLA-A and B was considered to be of the order of magnitude 0.008. We will argue similarly to Vogel and Motulsky below. In case LD measure was observed to be 0.003 in pan-Europeans in the list of Mittal[15] it is mostly non-significant. If had reduced from 0.07 to 0.003 under recombination effect as shown by , then . Suppose a generation took 25 years, this means 10,000 years. The time span seems rather short in the history of humans. Thus observed linkage disequilibrium between HLA-A and B loci might indicate some sort of interactive selection.[12]

The presence of linkage disequilibrium between an HLA locus and a presumed major gene of disease susceptibility corresponds to any of the following phenomena:

  • Relative risk for the person having a specific HLA allele to become suffered from a particular disease is greater than 1.[16]
  • The HLA antigen frequency among patients exceeds more than that among a healthy population. This is evaluated by value[17] to exceed 0.
Table 3. Association of ankylosing spondylitis with HLA-B27 allele[18]
Ankylosing spondylitis Total
Patients Healthy controls
HLA alleles
  • 2×2 association table of patients and healthy controls with HLA alleles shows a significant deviation from the equilibrium state deduced from the marginal frequencies.

(1) Relative risk

Relative risk of an HLA allele for a disease is approximated by the odds ratio in the 2×2 association table of the allele with the disease. Table 3 shows association of HLA-B27 with ankylosing spondylitis among a Dutch population.[18] Relative risk of this allele is approximated by

Woolf's method[19] is applied to see if there is statistical significance. Let



follows the chi-square distribution with . In the data of Table 3, a significant association exists at the 0.1% level. Haldane's[20] modification applies to the case when either of is zero, where and are replaced with



Table 4. Association of HLA alleles with rheumatic and autoimmune diseases among white populations[16]
Disease HLA allele Relative risk (%) FAD (%) FAP (%)
Ankylosing spondylitis B27 90 90 8 0.89
Reactive arthritis B27 40 70 8 0.67
Spondylitis in inflammatory bowel disease B27 10 50 8 0.46
Rheumatoid arthritis DR4 6 70 30 0.57
Systemic lupus erythematosus DR3 3 45 20 0.31
Multiple sclerosis DR2 4 60 20 0.5
Diabetes mellitus type 1 DR4 6 75 30 0.64

In Table 4, some examples of association between HLA alleles and diseases are presented.[16]

(1a) Allele frequency excess among patients over controls

Even high relative risks between HLA alleles and the diseases were observed, only the magnitude of relative risk would not be able to determine the strength of association.[17] value is expressed by

where and are HLA allele frequencies among patients and healthy populations, respectively.[17] In Table 4, column was added in this quotation. Putting aside 2 diseases with high relative risks both of which are also with high values, among other diseases, juvenile diabetes mellitus (type 1) has a strong association with DR4 even with a low relative risk.

(2) Discrepancies from expected values from marginal frequencies in 2×2 association table of HLA alleles and disease

This can be confirmed by test calculating

where . For data with small sample size, such as no marginal total is greater than 15 (and consequently ), one should utilize Yates's correction for continuity or Fisher's exact test.[21]


A comparison of different measures of LD is provided by Devlin & Risch[22]

The International HapMap Project enables the study of LD in human populations online. The Ensembl project integrates HapMap data with other genetic information from dbSNP.

Analysis software[edit]

  • PLINK – whole genome association analysis toolset, which can calculate LD among other things
  • LDHat
  • Haploview
  • LdCompare[23]— open-source software for calculating LD.
  • SNP and Variation Suite – commercial software with interactive LD plot.
  • GOLD – Graphical Overview of Linkage Disequilibrium
  • TASSEL – software to evaluate linkage disequilibrium, traits associations, and evolutionary patterns
  • rAggr – finds proxy markers (SNPs and indels) that are in linkage disequilibrium with a set of queried markers, using the 1000 Genomes Project and HapMap genotype databases.
  • SNeP – Fast computation of LD and Ne for large genotype datasets in PLINK format.
  • LDlink – A suite of web-based applications to easily and efficiently explore linkage disequilibrium in population subgroups. All population genotype data originates from Phase 3 of the 1000 Genomes Project and variant RS numbers are indexed based on dbSNP build 151.
  • Bcftools – utilities for variant calling and manipulating VCFs and BCFs.

Simulation software[edit]

  • Haploid — a C library for population genetic simulation (GPL)

See also[edit]


  1. ^ a b c Slatkin, Montgomery (June 2008). "Linkage disequilibrium — understanding the evolutionary past and mapping the medical future". Nature Reviews Genetics. 9 (6): 477–485. doi:10.1038/nrg2361. PMC 5124487. PMID 18427557.
  2. ^ Falconer, DS; Mackay, TFC (1996). Introduction to Quantitative Genetics (4th ed.). Harlow, Essex, UK: Addison Wesley Longman. ISBN 978-0-582-24302-6.
  3. ^ Slatkin, Montgomery (June 2008). "Linkage disequilibrium — understanding the evolutionary past and mapping the medical future". Nature Reviews Genetics. 9 (6): 477–485. doi:10.1038/nrg2361. ISSN 1471-0056. PMC 5124487. PMID 18427557.
  4. ^ Calabrese, Barbara (2019-01-01), Ranganathan, Shoba; Gribskov, Michael; Nakai, Kenta; Schönbach, Christian (eds.), "Linkage Disequilibrium", Encyclopedia of Bioinformatics and Computational Biology, Oxford: Academic Press, pp. 763–765, doi:10.1016/b978-0-12-809633-8.20234-3, ISBN 978-0-12-811432-2, retrieved 2020-10-21
  5. ^ Lewontin, R. C. (1964). "The interaction of selection and linkage. I. General considerations; heterotic models". Genetics. 49 (1): 49–67. PMC 1210557. PMID 17248194.
  6. ^ Hill, W.G. & Robertson, A. (1968). "Linkage disequilibrium in finite populations". Theoretical and Applied Genetics. 38 (6): 226–231. PMID 24442307.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  7. ^ VanLiere, J.M. & Rosenberg, N.A. (2008). "Mathematical properties of the measure of linkage disequilibrium". Theoretical Population Biology. 74 (1): 130–137. PMC 2580747. PMID 18572214.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  8. ^ Smith, R.D. (2020). "The nonlinear structure of linkage disequilibrium". Theoretical Population Biology. 134: 160–170. PMID 32222435.
  9. ^ Robbins, R.B. (1 July 1918). "Some applications of mathematics to breeding problems III". Genetics. 3 (4): 375–389. PMC 1200443. PMID 17245911.
  10. ^ R.C. Lewontin & K. Kojima (1960). "The evolutionary dynamics of complex polymorphisms". Evolution. 14 (4): 458–472. doi:10.2307/2405995. ISSN 0014-3820. JSTOR 2405995.
  11. ^ a b Svejgaard A, Hauge M, Jersild C, Plaz P, Ryder LP, Staub Nielsen L, Thomsen M (1979). The HLA System: An Introductory Survey, 2nd ed. Basel; London; Chichester: Karger; Distributed by Wiley, ISBN 3805530498(pbk).
  12. ^ a b c d Vogel F, Motulsky AG (1997). Human Genetics : Problems and Approaches, 3rd ed.Berlin; London: Springer, ISBN 3-540-60290-9.
  13. ^ Mittal KK, Hasegawa T, Ting A, Mickey MR, Terasaki PI (1973). "Genetic variation in the HL-A system between Ainus, Japanese, and Caucasians," In Dausset J, Colombani J, eds. Histocompatibility Testing, 1972, pp. 187–195, Copenhagen: Munksgaard, ISBN 87-16-01101-5.
  14. ^ Yasuda, N; Tsuji, K (June 1975). "A counting method of maximum likelihood for estimating haplotype frequency in the HL-A system". Jinrui Idengaku Zasshi. 20 (1): 1–15. PMID 1237691.
  15. ^ a b c d Mittal, KK (1976). "The HLA polymorphism and susceptibility to disease". Vox Sang. 31?-73 (3): 161–73. doi:10.1111/j.1423-0410.1976.tb02206.x. PMID 969389.
  16. ^ a b c Gregersen PK (2009). "Genetics of rheumatic diseases," InFirestein GS, Budd RC, Harris ED Jr, McInnes IB, Ruddy S, Sergent JS, eds. (2009). Kelley's Textbook of Rheumatology, pp. 305-321, Philadelphia, PA: Saunders/Elsevier, ISBN 978-1-4160-3285-4.
  17. ^ a b c Bengtsson, BO; Thomson, G (November 1981). "Measuring the strength of associations between HLA antigens and diseases". Tissue Antigens. 18 (5): 356–63. doi:10.1111/j.1399-0039.1981.tb01404.x. PMID 7344182.
  18. ^ a b Nijenhuis, LE (September 1977). "Genetic considerations on association between HLA and disease". Hum. Genet. 38 (2): 175–82. doi:10.1007/bf00527400. PMID 908564.
  19. ^ Woolf, B (June 1955). "On estimating the relation between blood group and disease". Ann. Hum. Genet. 19 (4): 251–3. doi:10.1111/j.1469-1809.1955.tb01348.x. PMID 14388528.
  20. ^ Haldane, JB (May 1956). "The estimation and significance of the logarithm of a ratio of frequencies". Ann. Hum. Genet. 20 (4): 309–11. doi:10.1111/j.1469-1809.1955.tb01285.x. PMID 13314400.
  21. ^ Sokal RR, Rohlf FJ (1981). Biometry: The Principles and Practice of Statistics in Biological Research. Oxford: W.H. Freeman, ISBN 0-7167-1254-7.
  22. ^ Devlin B.; Risch N. (1995). "A Comparison of Linkage Disequilibrium Measures for Fine-Scale Mapping" (PDF). Genomics. 29 (2): 311–322. CiteSeerX doi:10.1006/geno.1995.9003. PMID 8666377.
  23. ^ Hao K.; Di X.; Cawley S. (2007). "LdCompare: rapid computation of single – and multiple-marker r2 and genetic coverage". Bioinformatics. 23 (2): 252–254. doi:10.1093/bioinformatics/btl574. PMID 17148510.

Further reading[edit]