Modern computational approaches for analysing molecular genetic variation data

Key Points

  • An explosive growth is occurring in both the quantity of molecular data that are being collected and the efficiency of the computational machinery that is commonly used to analyse those data.

  • One of the traditional analytical paradigms has been based on models that are designed to capture the key features of the evolutionary processes.

  • A variety of approaches exist, and the choice of the most appropriate method, and model, depends on the features of the problem of interest.

  • The rapid growth in the size of data leads to an increasing computational burden for existing methods. In many cases this burden becomes overwhelming.

  • This has motivated a move away from exact methods (often because exact answers cannot be calculated) and towards more approximate methods. The principle is that it is better to obtain a rough answer than to seek an exact answer that cannot be computed in a reasonable time.

  • There will be a continuing trend to move away from exact methods and towards approximate methods as the quantity and complexity of data continue to grow.

  • Unfortunately, there is no 'one-size-fits-all' computational analysis method. We discuss a range of methods, but the performance of each will vary from problem to problem.


An explosive growth is occurring in the quantity, quality and complexity of molecular variation data that are being collected. Historically, such data have been analysed by using model-based methods. Models are useful for sharpening intuition, for explanation and for prediction: they add to our understanding of how the data were formed, and they can provide quantitative answers to questions of interest. We outline some of these model-based approaches, including the coalescent, and discuss the applicability of the computational methods that are necessary given the highly complex nature of current and future data sets.

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The authors were supported in part by two grants from the US National Institutes of Health. S.T. is a Royal Society-Wolfson Research Merit Award holder. We thank the reviewers for helpful comments on an earlier version of the manuscript.

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Restriction fragment length polymorphisms

Variations between individuals in the lengths of DNA regions that are cut by a particular endonuclease.

Microsatellite marker loci

Polymorphic loci at which short DNA sequences are repeated a varying number of times.

Stochastic model

A model that is used to describe the behaviour of a random process.


A popular probabilistic model for the evolution of 'individuals'. Individuals might be single nucleotides, mitochondrial DNA, chromosomes and so on, depending on the context.

Selective sweep

The increase in the frequency of an allele (and closely linked chromosomal segments) that is caused by selection for the allele. Sweeps initially reduce variation and subsequently lead to increased homozygosity.


The probability of the data under a particular model, viewed as a function of the parameters of that model (note that data discussed in this paper are discrete).

Mitochondrial Eve

The most recent maternal common ancestor of the entire human mitochondrial population.

Gene conversion

A non-reciprocal recombination process that results in the alteration of the sequence of a gene to that of its homologue during meiosis.


Gene flow between differentiated populations.

Maximum likelihood

A statistical analysis in which one aims to find the parameter value that maximizes the likelihood of the data.

Test statistic

A numerical summary of the data that is used to measure support for a null hypothesis. Either the test statistic has a known probability distribution (such as χ2) under the null hypothesis, or its null distribution is approximated computationally.

Tajima's D

A statistic that compares the observed nucleotide diversity to what is expected under a neutral, constant population-sized model.

Prior distribution

The distribution of likely parameter values before any data are examined.

Posterior distribution

The distribution that is proportional to the product of the likelihood and prior distribution.


The range of values for which the probability is non-zero.

Summary statistics

A statistic that tries to capture a complicated data set in a simpler way. An example is the use of the number of segregating sites as a surrogate for a set of DNA fragments.

Markov process

One in which the probability of the next state depends solely on the previous state, and not on the sequence of states before it.


The state in which a process has become independent of its starting position and has settled into its long-term behaviour. In an MCMC context, the process is typically assumed to be stationary at the end of a 'burn-in' period.

Local maxima

A local region in which a distribution takes a value that is higher than those taken at other nearby points, but which is lower than at least one value taken in some other, more distant region.


The statistic S is sufficient for the parameter η if the probability of the data, given S and η , does not depend on η.


The sequence of bases along a single copy of (typically, part of) a chromosome.

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Marjoram, P., Tavaré, S. Modern computational approaches for analysing molecular genetic variation data. Nat Rev Genet 7, 759–770 (2006).

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