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Table 3 Factors affecting (a) PCR success and (b) levels of polymorphism, analyzed using generalized linear models

From: A second generation framework for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica

a)     
Model term Estimate Standard error z value P
Pipeline    −0.348 0.728
 CMiB 0 0   
 read2Marker −0.1074 0.3090   
Primer location    −0.513 0.6081
 coding 0 0   
 others −0.1590 0.3101   
Sum of primer melting temperature 0.1420 0.1191 1.192 0.2333
Expected PCR product size −0.0033 0.0015 −2.252 0.0244
b)     
Model term Estimate Standard error z value P
Pipeline    0.473 0.636
 CMiB 0 0   
 read2Marker 0.0727 0.1536   
SSR location    −1.486 0.137
 coding 0 0   
 others −0.2824 0.1901   
Maximum No. of SSR repeats 0.1344 0.0233 5.782 7.39E-09
SSR motif (number of SSR repeat unit) −0.0966 0.1534 −0.63 0.529
  1. PCR success was coded using a variable that took a value of 1 for success and 0 for failure. The primer melting temperature (Tm) was summed for both primers in a pair. The R functions called when estimating PCR success were: glm(formula = PCR.success ~ Pipeline + Primer.location + Sum.of.primer.Tm + Expected.PCR.product.size, family = binomial). The level of polymorphism was expressed in terms of number of alleles per locus (Na) and was analyzed using the following function calls in R: glm(formula = Na ~ Pipeline + SSR.location + Maximum.No..of.SSR.repeats + SSR.motif, family = poisson). SSR motif corresponded to the number of bases in the SSR repeat unit; di-, tri-, tetra-, hexa-, and penta-SSRs were coded as 2, 3, 4, 5 and 6, respectively.