Mapping 250K/500K SNP assay

chapter 7 | Analysis Workflow 197
Genomic DNA Quality
Genomic DNA should be prepared following the guidelines in
Chapter 3 of this manual; DNA prepared outside these guidelines
(e.g., degraded DNA, nicked DNA or DNA with inhibitors) may
produce lower Call Rates without necessarily reducing accuracy. A gel
image of the DNA before restriction digestion should be used to
evaluate DNA quality. Direct comparison to the Reference Genomic
DNA 103 control in a 2% agarose gel is one way to accomplish this.
If an alternate genomic DNA preparation method is used, it is highly
recommended that a small pilot experiment be conducted to evaluate
reproducibility and accuracy of genotype calls.
Deviation from Assay Protocol
A problem in any step of the assay may lead to a decreased Call Rate.
The gel images produced before DNA digestion and before PCR
cleanup, the PCR yield after cleanup, and a gel image after
fragmentation can be used to identify problematic steps. Consult
Chapter 8, Troubleshooting for further information.
At a minimum, a PCR negative control (water instead of DNA
template) should be incorporated into each group of samples
processed. The Reference Genomic DNA 103 is included in the assay
kit as a positive process control.
DETECTING SAMPLE CONTAMINATION
Monitoring sample contamination is a critical component of sample
processing in SNP genotyping. While detection of mixed or
contaminated samples is relatively straightforward with multi-allelic
markers such as microsatellite markers, it can be more of a challenge
for bi-allelic markers. The presence of contamination in a sample
reduces genotyping accuracy and therefore genetic power. Guidelines
are described in this manual that aid in reducing contamination (i.e.,
lab set-up) as well as detecting contamination through gel
electrophoresis (i.e., process negative controls and PCR negative
controls) and analysis metrics in GTYPE (GTYPE Report metrics).
Because of the importance of detecting mixed or contaminated
samples, GTYPE has an additional algorithm to supplement the
Dynamic Model algorithm to help with sample contamination