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The high variability of microsatellites aids error detection as most errors will result in Mendelian incompatibilities.
Because of the high variability of microsatellites within a population, they are ideally suited for QTL analysis.
Thus, genetic variation was congruent with the high variability of microsatellites used, indicating temporal stability among consecutive year-classes.
Studies have shown that intra-allelic variability of microsatellites is significantly associated with selective force that maintains microsatellite loci in genome.
Bearing this in mind, we compared the genetic variability of microsatellites between these populations, searching for possible associations between neutral genetic variability and their adaptive response.
It is also worth noting that the greater variability of microsatellites means that they resulted in a much higher number of informative meioses than the SNPs (see Figure 1), which more than offsets the higher failure rate.
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In the first scenario (Ne≤50), given the number and variability of microsatellite loci employed in this study, we would be able to detect a critical reduction of Ne with a rather high probability as pointed out above.
The high variability of microsatellite markers and their straightforward analysis using the polymerase chain reaction (PCR) have led to their frequent application in studies on natural populations.
Many features that shape genome evolution generally, such as nucleotide composition, may play a large role in the variability of microsatellite density across the genome (e.g. [ 14, 15]).
There is evidence that there is no difference between the variability of microsatellite markers developed from non-EST and EST sequences but other studies suggest non-EST markers may be more variable than those from ESTs (cf. [ 37- 39]).
The variability of microsatellite number found in nematode genomes was well explained by genome size for M. incognita, M. hapla, C. elegans and P. pacificus (r2 = 0.95, F1,2 = 37.5, p = 0.03), but not when B. malayi was included (r2 = 0.10, F1,3 = 0.34, p = 0.6) (Table 1).
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