A simuPOP population consists of individuals of the same genotype structure, which includes properties such as number of homologous sets of chromosomes (ploidy), number of chromosomes, and names and locations of markers on each chromosome. In addition to basic information such as genotypes and sex, individuals can have arbitray auxillary values as information fields. Individuals in a population can be divided into subpopulations that can be further grouped into virtual subpopulations according to individual properties such as sex, affection status, or arbitrary auxiliary information such as age. Whereas subpopulations define boundaries of individuals that restrict the flow of individuals and their genotypes (mating happens within subpopulations), virtual subpopulations are groups of individuals who share the same properties, with membership of individuals change easily with change of individual properties.
Figure: A life cycle of an evolutionary process
Illustration of the discrete-generation evolutionary model used by simuPOP.
Operators are Python objects that act on a population. They can be applied to a population before or after mating during a life cycle of an evolutionary process (Figure fig_life_cycle), or to parents and offspring during the production of each offspring. Arbitrary numbers of operators can be applied to an evolving population.
A simuPOP mating scheme is responsible for choosing parent or parents from a parental (virtual) subpopulation and for populating an offspring subpopulation. simuPOP provides a number of pre-defined homogeneous mating schemes, such as random, monogamous or polygamous mating, selfing, and haplodiploid mating in hymenoptera. More complicated nonrandom mating schemes such as mating in age- structured populations can be constructed using heterogeneous mating schemes, which applies multiple homogeneous mating schemes to different (virtual) subpopulations.
simuPOP evolves a population generation by generation, following the evolutionary cycle depicted in Figure fig_life_cycle. Briefly speaking, a number of operators such as a KAlleleMutator are applied to a population before a mating scheme repeatedly chooses a parent or parents to produce offspring. During-mating operators such as Recombinator can be applied by a mating scheme to transmit parental genotype to offspring. After an offspring population is populated, other operators can be applied, for example, to calculate and output population statistics. The offspring population will then become the parental population of the next evolutionary cycle. Many simuPOP operators can be applied in different stages so the type of an operator is determined by the stage at which it is applied. Several populations, or replicates of a single population, could form a simulator and evolve together.
Example: A simple example
>>> import simuPOP as sim >>> pop = sim.Population(size=1000, loci=2) >>> pop.evolve( ... initOps=[ ... sim.InitSex(), ... sim.InitGenotype(genotype=[1, 2, 2, 1]) ... ], ... matingScheme=sim.RandomMating(ops=sim.Recombinator(rates=0.01)), ... postOps=[ ... sim.Stat(LD=[0, 1], step=10), ... sim.PyEval(r"'%.2f\n' % LD", step=10), ... ], ... gen=100 ... ) 0.25 0.22 0.21 0.19 0.17 0.15 0.15 0.12 0.10 0.10 100L
Some of these concepts are demonstrated in Example simple_example, where a standard diploid Wright-Fisher model with recombination is simulated. The first line imports the standard simuPOP module. The second line creates a diploid population with 1000 individuals, each having one chromosome with two loci. The evolve() function evolves the population using a random mating scheme and four operators.
Operators InitSex and InitGenotype are applied at the beginning of the evolutionary process. Operator InitSex initializes individual sex randomly and InitGenotype initializes all individuals with the same genotype 12/21. The populations are then evolved for 100 generations. A random mating scheme is used to generate offspring. Instead of using the default Mendelian genotype transmitter, a Recombinator (during-mating operator) is used to recombine parental chromosomes with the given recombination rate 0.01 during the generation of offspring. The other operators are applied to the offspring generation (post-mating) at every 10 generations (parameter step). Operator Stat calculates linkage disequilibrium between the first and second loci. The results of this operator are stored in a local variable space of the Population. The last operator PyEval outputs calculated linkage disequilibrium values with a trailing new line. The result represents the decay of linkage disequilibrium of this population at 10 generation intervals. The return value of the evolve function, which is the number of evolved generations, is also printed.