Template:Infobox multi locus allele clusters

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Infobox

Multi Locus Allele Clusters

In a haploid population, when a single locus is considered (blue), with two alleles, wild-type (+) and mutant (-) we can see a differential geographical distribution between Population I and Population II, but there is a 30% chance of wrongly assigning any individual to either population based on a single allele.
× + -
Population I 70% 30%
Population II 30% 70%

For three loci blue, red and green, it becomes apparent that there is a correlation between certain allele frequencies. In this example Population I displays a correlation between wild-type blue (+) 70%, mutant red (-) 70% and wild type green (+) 70%. Population II has a correlation between the -, + and - alleles, each having a 70% frequency in this population. The genetic variation remains the same in these populations, irresepctive of the allele examined, but using a three locus approach, there is a much reduced chance of wrongly assigning any individual to a given population.

× + - + - + -
Population I 70% 30% 30% 70% 70% 30%
Population II 30% 70% 70% 30% 30% 70%

For an organism of genotype +/-/+, for each locus the chance of missclassification is 0.3 (30%), but when all three loci are take into account, the organism can be assigned to Population I with a 0.3x0.3x0.3 chance of error, that is a 0.027 (2.7%) chance of error. The two populations still share exactly the same alleles, but the frequency of these alleles varies between the populations.

Using modern computer software and the abundance of genetic data now available, it is possible not only to distinguish such correlations for hundreds or even thousands of alleles, which form clusters, it is also possible to assign individuals to given populations with very little chance of error. It should be noted, however, that genes tend to vary clinally, and there are likely to be intermediate populations that reside in the geographical areas between our sample populations (Population III, for example, may lie equidistantly from Population I and Population II). In this case it may well be that Population III may display characteristics of both population I and Population II. For example Population III may be defined thus:

× + - + - + -
Population III 50% 50% 50% 50% 50% 50%

In which case any individual from Population III is likely to be misclassified equally into either Population I or Population II.(Edwards (2003)Kittles and Weiss (2003))