Skip to main content

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.