Population concordance ratio

An interesting problem in population genetics is the following: how often are two individuals from a population A more similar to each other than either of them is to an individual from another population B?

Suppose you have a similarity function for individuals, sim(a, b)

(In my experiments I will use identity-by-state (IBS) as calculated by PLINK (--cluster --matrix) as a measure of similarity, but any symmetrical similarity function (that is, sim(a,b)=sim(b,a)) will do.)

We want to calculate the rate at which the following condition occurs:

If this condition holds, then a and a' from population A are more similar to each other than either of them is to an individual b from population B. We then say that the trio of individuals is concordant.

I will use the indicator function I(a, a', b) = 1 in case of a concordant trio, and =0 otherwise.

I can then estimate the probability of concordance, if I have n individuals from A and m from B as follows:

The rationale behind this formula is straightforward: we are counting the number of all concordant trios, and dividing by n(n-1)m/2 since there are n(n-1)/2 pairs of individuals from A, and each pair is compared against all m individuals from B.

The expected value of this concordance ratio can vary between 0.25 and 1:

• It is 1 if the two populations are so well-differentiated so that every trio is concordant.
• On the other hand, if the two populations are genetically identical, then each similarity comparison is equivalent to a coin toss (probability = 0.5) and we are testing this condition for two different individuals from A: hence the probability of concordance for each trio is 0.5*0.5 = 0.25.

In a finite sample of individuals it is possible that the concordance ratio estimate may actually be lower than 0.25.

An interesting property of the concordance ratio is its asymmetry, that is:

We will see how this property gives some useful insight in some of the following examples.

#1. Han vs. Yoruba

This is based on the Stanford HGDP set, including only individuals as recommended by Rosenberg (2006) in his H952 set. For each experiment only SNPs with at least a 99% genotyping rate have been retained.

The first experiment is designed to showcase the concordance ratio in two well-differentiated human populations, 44 Han Chinese and 21 Yoruba Nigerians. The analysis is based on 617,602 SNPs.

That is, two Han Chinese are always more similar to each other than to a Yoruba and vice versa.

I repeated the experiment, sequentially thinning the market set randomly by a factor of 10 using PLINK's --thin 0.1 argument. Concordance remained 1 for ~61k, ~6k, and ~600 markers, and became different than 1 (but still greater than 0.95) with 50 SNPs.

Genome-wide, or, across a sizeable number of markers concordance of Han vs. Yoruba and vice versa is perfect, while for a few random SNPs it may not be.

#2. Britons vs. Mexicans

In the next experiment, I have used 90 Britons (GBR) and 69 Mexican Americans from Los Angeles (MXL) from the 1000 Genomes Project. I have included only SNPs with 99%+ genotyping rate that are also included in the HGDP Stanford data, for a total of 351,521 markers.

Notice the previously mentioned asymmetry: two Britons are virtually always closer to each other than they are to a Mexican, but a Mexican is sometimes closer to a Briton than he is to a fellow Mexican. This is due to the fact that Mexicans have variable European admixture, so a substantially European-admixed Mexican may be closer to a Briton than he is to one of his substantially Amerindian-admixed compatriots.

#3: Various Europeans

In the final experiment, I included HGDP European populations, together with 12 Dodecad Project Greeks. The analysis is based on 492,176 SNPs. Each row in the following table represents the first argument of the concordance ratio function, and each column the second one.

Here is a way to read the table, using Greek_D as an example:

• The last row represents a test in which pairs of Greeks were compared against individuals from the population of each column. These comparisons were concordant 84.4% of the time against Russians (the most distant population), and 37.5% of the time against Tuscans (the closest one).
• The last column represents in which Greeks were used as an "outgroup" for comparison against pairs of individuals from each row. These comparisons were concordant (~1) for most populations, except the Tuscan (47.9%), North Italian (71.5%), and Adygei (81.9%)

Let's do the same using French as an example:

• With a pair of French individuals against individuals from other populations, concordance ranged between 36.2% (for North Italians) to 95.8% (for Adygei)
• When French individuals were used as an outgroup to compare against pairs of individuals from other populations, concordance ranged between 63.1% (for North Italians) to 100% (for Sardinians). The latter means that a pair of Sardinians is always closer to each other than to a French sample (or, at least, an HGDP French one).

Conclusion

The study of concordance is an interesting thought experiment that illustrates how genome-wide comparisons between individuals show the following:

1. Two individuals from a homogeneous population are virtually always more similar to each other than to an individual from a genetically differentiated population
2. Two individuals from a population may, or may not, be more similar to each other than to an individual from a genetically related population
3. More variable populations are usually more discordant with respect to other populations, whereas very homogeneous populations tend to be concordant

The concordance ratio is useful for personal genomics customers, as it puts their IBS similarities to various other individuals in perspective. For example, a Greek should not be surprised if he matches a particular Tuscan more than he does a fellow Greek, nor should he seek mysterious Italian ancestors because of it, as such a discordant result occurs frequently.

The concordance ratio is also useful because it provides a truly model-free test of population differentiation:

It is different from techniques such as PCA which allow the separability of individuals from different populations by projecting them on a number of dimensions, the first few of which are usually correlated with the inter-population fraction of genetic diversity. Hence, individuals that appear well-separated on a few PCA dimensions may in fact be overall more genetically similar to individuals from other populations across the full marker set. The concordance ratio avoids any accusation of privileging aspects of the genome (the ones that differentiate populations), as it is based on a single genome-wide similarity function for individuals.

It is also different from clustering algorithms such as ADMIXTURE that infer allele frequencies in putative ancestral populations, again implicitly using markers with high frequency differences to estimate admixture proportions. Hence, a "match" in a marker with low population differentiation is treated differently as a source of evidence than a match in a marker of strong population differentiation. The concordance ratio avoids this issue by using a single similarity function for individuals that does not privilege one marker over another.

R Code

Code for the calculation of the concordance ratio ratio can be downloaded from here as an R function. Two files are required:

• A symmetrical similarity matrix, as output by plink --matrix --cluster command. Any similarity matrix file in the PLINK MIBS format will do.
• A file in which each row has a population name and the number of individuals from that population.

An example of the latter file count.txt for Experiment #3 is:

North_Italian 12

Russian 25

Orcadian 15

Sardinian 28

Tuscan 8

French 28

French_Basque 24

Adygei 17

Greek_D 12

Of course, individuals must appear in that order in the plink file, i.e., first the 12 North Italians, then the 25 Russians, etc.

Assuming you have such a file, e.g., in binary BED/BIM/FAM format, you first calculate the IBS matrix:

plink --matrix --cluster --bfile datafile

This creates a plink.mibs file. Then, in R, after changing to the appropriate directory, where plink.mibs, count.txt and the source code gamma.r is, you enter:

source("gamma.r")

gamma(simfile="plink.mibs", popfile="count.txt")

PS: The concordance ratio should not be confused with Witherspoon's ω fraction. That is defined by comparing all pairs of between- and within- population distances, and ranges between 0 (highest concordance in my terminology) and 0.5 (lowest concordance). The concordance ratio, on the other hand, tests all possible trios of individuals, and it also has the asymmetrical property explained above.