Linkage disequilibrium

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"Graph showing evolution of linkage disequilibrium in a haploid population with positive assortative mating (inbreeding); graph shows the trajectories of 1,024 populations with randomly generated initial allele frequencies; trajectories beginning close to 0 slope away from 0 sharply then decline to 0 asymptotically, while trajectories beginning near 0 diminish to 0"
A simulation showing the evolution of linkage disequilibrium in randomly generated haploid populations with positive assortative mating

In population genetics, linkage disequilibrium is the non-random association of alleles at two or more loci, that descend from single, ancestral chromosomes.[1] Linkage disequilibrium is wholly a measurement of proximal genomic space. It is necessary to refer to this as gametic phase disequilibrium[2] or simply gametic disequilibrium because it is described through DNA recombination. In other words, linkage disequilibrium is the occurrence of some combinations of alleles or genetic markers in a population more often or less often than would be expected from a random formation of haplotypes from alleles based on their frequencies. It is a second order phenomenon derived from linkage, which is the presence of two or more loci on a chromosome with limited recombination between them. The amount of linkage disequilibrium depends on the difference between observed allelic frequencies and those expected from a homogenous, randomly distributed model. Populations where combinations of alleles or genotypes can be found in the expected proportions are said to be in linkage equilibrium.

The level of linkage disequilibrium is influenced by a number of factors, including genetic linkage, selection, the rate of recombination, the rate of mutation, genetic drift, non-random mating, and population structure. A limiting example of the effect of rate of recombination may be seen in some organisms (such as bacteria) that reproduce asexually and hence exhibit no recombination to break down the linkage disequilibrium. An example of the effect of population structure is the phenomenon of Finnish disease heritage, which is attributed to a population bottleneck.

Definition[edit]

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

Haplotype Frequency
A_1B_1 x_{11}
A_1B_2 x_{12}
A_2B_1 x_{21}
A_2B_2 x_{22}

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

Allele Frequency
A_1 p_{1}=x_{11}+x_{12}
A_2 p_{2}=x_{21}+x_{22}
B_1 q_{1}=x_{11}+x_{21}
B_2 q_{2}=x_{12}+x_{22}

If the two loci and the alleles are independent from each other, then one can express the observation A_1B_1 as "A_1 is found and B_1 is found". The table above lists the frequencies for A_1, p_1, and forB_1, q_1, hence the frequency of A_1B_1 is x_{11}, and according to the rules of elementary statistics x_{11} = p_{1} q_{1}.

The deviation of the observed frequency of a haplotype from the expected is a quantity[3] called the linkage disequilibrium[4] and is commonly denoted by a capital D:

D = x_{11} - p_1q_1

In the genetic literature the phrase "two alleles are in LD" usually means that D0. Contrariwise, "linkage equilibrium" means D = 0.

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

A_1 A_2 Total
B_1 x_{11}=p_1q_1+D     x_{21}=p_2q_1-D    q_1
B_2 x_{12}=p_1q_2-D x_{22}=p_2q_2+D q_2
Total    p_1 p_2 1

D is easy to calculate with, but has the disadvantage of depending on the frequencies of the alleles. This is evident since frequencies are between 0 and 1. If any locus has an allele frequency 0 or 1 no disequilibrium D can be observed. When the allelic frequencies are 0.5, the disequilibrium D is maximal. Lewontin[5] suggested normalising D by dividing it by the theoretical maximum for the observed allele frequencies.

Thus:

D' = \tfrac{D}{D_\max}

where

D_\max = \begin{cases}
\min(p_1q_1,\,p_2q_2) & \text{when } D < 0\\
\min(p_1q_2,\,p_2q_1) & \text{when } D > 0
\end{cases}

Another measure of LD which is an alternative to D' is the correlation coefficient between pairs of loci, expressed as

r=\frac{D}{\sqrt{p_1p_2q_1q_2}}.

This is also adjusted to the loci having different allele frequencies.

In summary, linkage disequilibrium reflects the difference between the expected haplotype frequencies under the assumption of independence, and observed haplotype frequencies. A value of 0 for D' indicates that the examined loci are in fact independent of one another, while a value of 1 demonstrates complete dependency.

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 D converges to zero along the time axis at a rate depending on the magnitude of the recombination rate c between the two loci.

Using the notation above, D= x_{11}-p_1 q_1, we can demonstrate this convergence to zero as follows. In the next generation, x_{11}', the frequency of the haplotype A_1 B_1, becomes

x_{11}' = (1-c)\,x_{11} + c\,p_1 q_1

This follows because a fraction (1-c) of the haplotypes in the offspring have not recombined, and are thus copies of a random haplotype in their parents. A fraction x_{11} of those are A_1 B_1. A fraction c have recombined these two loci. If the parents result from random mating, the probability of the copy at locus A having allele A_1 is p_1 and the probability of the copy at locus B having allele B_1 is q_1, 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

x_{11}' - p_1 q_1 = (1-c)\,(x_{11} - p_1 q_1)

so that

D_1 = (1-c)\;D_0

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

D_n = (1-c)^n\; D_0.

If n \to \infty, then (1-c)^n \to 0 so that D_n 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 D to zero.

Example: Human Leukocyte Antigen (HLA) alleles[edit]

HLA constitutes a group of cell surface antigens as 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[6] referred to by Vogel and Motulsky (1997).[7]

Table 1. Association of HLA-A1 and B8 in unrelated Danes[6]
Antigen j Total
+ -
B8^{+} B8^{-}
Antigen i + A1^{+} a=376 b=237 C
- A1^{-} c=91 d=1265 D
Total A B N
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 2x2 table to the right.[7][8][9][10]

expression (+) frequency of antigen i :

pf_i = C/N = 0.311\! ;

expression (+) frequency of antigen j :

pf_j = A/N = 0.237\! ;

frequency of gene i :

gf_i = 1 - \sqrt{1 - pf_i} = 0.170\! ,

and

hf_{ij} = \text{estimated frequency of haplotype } ij = gf_i \; gf_j = 0.0215\! .

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

o[hf_{xy}]=\sqrt{d/N}

and the estimated frequency of haplotype xy is

e[hf_{xy}]=\sqrt{(D/N)(B/N)}.

Then LD measure \Delta_{ij} is expressed as

\Delta_{ij}=o[hf_{xy}]-e[hf_{xy}]=\frac{\sqrt{Nd}-\sqrt{BD}}{N}=0.0769.

Standard errors SEs are obtained as follows:

SE\text{ of }gf_i=\sqrt{C}/(2N)=0.00628,
SE\text{ of }hf_{ij}=\sqrt{\frac{(1-\sqrt{d/B})(1-\sqrt{d/D})-hf_{ij}-hf_{ij}^2/2}{2N}}=0.00514
SE\text{ of }\Delta_{ij}=\frac{1}{2N}\sqrt{a-4N\Delta_{ij}\left (\frac{B+D}{2\sqrt{BD}}-\frac{\sqrt{BD}}{N}\right )}=0.00367.

Then, if

t=\Delta_{ij}/(SE\text{ of }\Delta_{ij})

exceeds 2 in its absolute value, the magnitude of \Delta_{ij} is large statistically significantly. 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[10]
HLA-A alleles i HLA-B alleles j \Delta_{ij} t
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.[10]

Vogel and Motulsky (1997)[7] 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[10] it is mostly non-significant. If \Delta_0 had reduced from 0.07 to 0.003 under recombination effect as shown by \Delta_n=(1-c)^n \Delta_0, then n\approx 400. 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.[7]

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.[11]
  • The HLA antigen frequency among patients exceeds more than that among a healthy population. This is evaluated by \delta value[12] to exceed 0.
Table 3. Association of ankylosing spondylitis with HLA-B27 allele[13]
Ankylosing spondylitis Total
Patients Healthy controls
HLA alleles B27^+ a=96 b=77 C
B27^- c=22 d=701 D
Total A B N
  • 2x2 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 2x2 association table of the allele with the disease. Table 3 shows association of HLA-B27 with ankylosing spondylitis among a Dutch population.[13] Relative risk xof this allele is approximated by

x=\frac{a/b}{c/d}=\frac{ad}{bc}\;(=39.7,\text{ in Table 3 }).

Woolf's method[14] is applied to see if there is statistical significance. Let

y=\ln (x)\;(=3.68)

and

\frac{1}{w}=\frac{1}{a}+\frac{1}{b}+\frac{1}{c}+\frac{1}{d}\;(=0.0703).

Then

\chi^2=wy^2\;\left [=193>\chi^2(p=0.001,\; df=1)=10.8 \right ]

follows the chi-square distribution with df=1. In the data of Table 3, the significant association exists at the 0.1% level. Haldane's[15] modification applies to the case when either ofa,\; b,\;c,\text{ and }d is zero, where replace x and 1/wwith

x=\frac{(a+1/2)(d+1/2)}{(b+1/2)(c+1/2)}

and

\frac{1}{w}=\frac{1}{a+1}+\frac{1}{b+1}+\frac{1}{c+1}+\frac{1}{d+1},

respectively.

Table 4. Association of HLA alleles with rheumatic and autoimmune diseases among white populations[11]
Disease HLA allele Relative risk (%) FAD (%) FAP (%) \delta
Ankylosing spondylitis B27 90 90 8 0.89
Reiter's syndrome 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.[11]

(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.[12]\delta value is expressed by

\delta=\frac{FAD-FAP}{1-FAP},\;\;0\le \delta \le 1,

where FAD and FAP are HLA allele frequencies among patients and healthy populations, respectively.[12] In Table 4, \delta column was added in this quotation. Putting aside 2 diseases with high relative risks both of which are also with high \delta values, among other diseases, juvenile diabetes mellitus (type 1) has a strong association with DR4 even with a low relative risk=6.

(2) Discrepancies from expected values from marginal frequencies in 2x2 association table of HLA alleles and disease

This can be confirmed by \chi^2 test calculating

\chi^2=\frac{(ad-bc)^2 N}{ABCD}\;(=336,\text{ for data in Table 3; }P<0.001).

where df=1. For data with small sample size, such as no marginal total is greater than 15 (and consequently N \le 30), one should utilize Yates's correction for continuity or Fisher's exact test.[16]

Resources[edit]

A comparison of different measures of LD is provided by Devlin & Risch [17]

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[18]— 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

Simulation software[edit]

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

See also[edit]

References[edit]

  1. ^ Reich, DE (2001). "Linkage disequilibrium in the human genome". Nature (Nautre Publishing Group) 411: 199–204. doi:10.1038/35075590. Retrieved 12 July 2013. 
  2. ^ Falconer, DS; Mackay, TFC (1996). Introduction to Quantitative Genetics (4th ed.). Harlow, Essex, UK: Addison Wesley Longman. ISBN 0-582-24302-5. 
  3. ^ Robbins, R.B. (1 July 1918). "Some applications of mathematics to breeding problems III". Genetics 3 (4): 375–389. PMC 1200443. PMID 17245911. 
  4. ^ R.C. Lewontin and K. Kojima (1960). "The evolutionary dynamics of complex polymorphisms". Evolution 14 (4): 458–472. doi:10.2307/2405995. ISSN 0014-3820. JSTOR 2405995. 
  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. ^ 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).
  7. ^ a b c d Vogel F, Motulsky AG (1997). Human Genetics : Problems and Approaches, 3rd ed.Berlin; London: Springer, ISBN 3-540-60290-9.
  8. ^ 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.
  9. ^ Yasuda N, Tsuji K (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.
  10. ^ a b c d Mittal KK (1976). "The HLA polymorphism and susceptibility to disease." Vox Sang 31: 161-173, PMID 969389.
  11. ^ 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.
  12. ^ a b c Bengtsson BO, Thomson G (1981). "Measuring the strength of associations between HLA antigens and diseases." Tissue Antigens18(5): 356-363, PMID 7344182.
  13. ^ a b Nijenhuis LE (1977). "Genetic considerations on association between HLA and disease." Hum Genet38(2): 175-182, PMID 908564.
  14. ^ Woolf B (1955). "On estimating the relation between blood group and disease." Ann Hum Genet 19(4): 251-253, PMID 14388528.
  15. ^ Haldane JB (1956). "The estimation and significance of the logarithm of a ratio of frequencies." Ann Hum Genet20(4): 309-311, PMID 13314400.
  16. ^ Sokal RR, Rohlf FJ (1981). Biometry: The Principles and Practice of Statistics in Biological Research. Oxford: W.H. Freeman, ISBN 0-7167-1254-7.
  17. ^ Devlin B., Risch N. (1995). "A Comparison of Linkage Disequilibrium Measures for Fine-Scale Mapping". Genomics 29 (2): 311–322. doi:10.1006/geno.1995.9003. PMID 8666377. 
  18. ^ 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]