Choice of marker

When comparing populations, it is important to realize that estimates of genetic diversity will vary depending on which molecular markers are used. This is because, as noted in earlier chapters, mutation rates vary both within and between genomes, and rapidly evolving markers such as microsatellites will generally reflect higher levels of diversity than more slowly evolving markers such as allozymes. Furthermore, comparisons between nuclear and organelle genomes may be influenced by past demographic histories; recall from Chapter 2 that the relatively small effective population sizes of mtDNA and cpDNA mean that mitochondrial and chloroplast diversity will be lost more rapidly than nuclear diversity following either permanent or temporary reductions in population size.

Discrepant estimates of genetic diversity were found in a study that used several different markers to compare European populations of the common carp (Cypri-nus carpio) (Kohlmann et al., 2003). According to data from 22 allozyme loci, Ho = 0.066, He = 0.062 and A = 1.232. Substantially higher values of Ho = 0.788, He = 0.764 and A = 5.75 were obtained from four microsatellite loci. An even greater difference was found in the mitochondrial genome. Mitochondrial haplotypes identified using PCR-RFLP revealed haplotype and nucleotide diversity estimates of zero. Genetic diversity in European common carp therefore ranges from nonexistent when estimated from mitochondrial markers to highly variable when estimated from microsatellite markers. This does not, however, mean that organelle markers always will be less diverse than nuclear markers. Red pine (Pinus resinosa) populations in Canada showed no allozyme variation and very little RAPD variation, but a survey of nine chloroplast microsatellite loci revealed 25 alleles and 23 different haplotypes in 159 individuals (Echt et al., 1998). Table 3.4 gives some other examples of genetic diversity estimates that vary depending on which markers were used.

Table 3.4 Comparisons of within-population variation, measured as He, based on several different types of markers. Microsatellite loci often are more variable than either allozyme or dominant markers




Gray mangrove

AFLP: 0.19

Maguire, Peakall and

(Avicennia marina)

Microsatellites: 0.78

Saenger (2002)

Russian couch grass

RAPD: 0.10

Sun et al. (1998)

(Elymus fibrosus)

Allozymes: 0.008

Microsatellites: 0.25

Wild and cultivated

AFLP: 0.32

Powell et al. (1996)

soybean (Glycine soja

RAPD: 0.31

and G. max)

Microsatellites: 0.60

Wild barley

AFLP: 0.16

Turpeinen et al. (2003)

(Hordeum spontaneum)

Microsatellites: 0.47

Lodgepole pine

RAPD: 0.43

Thomas et al. (1999)

(Pinus contorta)

Microsatellites: 0.73

Chinese native chickens

Allozymes: 0.221

Zhang et al. (2002)

(Gallus gallus domesticus)

RAPD: 0.263

Microsatellites: 0.759

Pink ling, a marine fish

Allozymes: 0.324

Ward et al. (2001)

(Genypterus blacodes)

Microsatellites: 0.823

Roe deer

Allozymes: 0.213

Wang and Schreiber (2001)

(Capreolus capreolus)

Microsatellites: 0.545

Regardless of how variable they are, the effective number of loci being screened will be the same as the actual number only if they are in linkage equilibrium, which will be true only if they segregate independently of each other during reproduction. Non-random association of alleles among loci is known as linkage disequilibrium; this can occur for a number of reasons, the most common being the proximity of two loci on a chromosome. When analysing data from multiple loci it is always necessary to test for linkage disequilibrium before ruling out the possibility that there are fewer independent loci for genetic analysis than anticipated. Linkage disequilibrium may also cause loci to behave in an unexpected manner, for example neutral alleles that are linked to selected alleles will appear non-neutral and are unlikely to be in HWE even if the population is large and mating is random.


Was this article helpful?

0 0

Post a comment