Abstract
Many seed testing laboratories currently use polymerase chain reaction (PCR) assays to test conventional crop seed for the adventitous presence of biotech trait seed. Seed organizations and companies are spending much time and resources assessing laboratories proficency in running PCR assays. Since many of these assays provide qualitative rather than quantitative results, laboratories must go through a significant effort to obtain adequate assay error estimates. Many sample-processing steps are very similar from assay to assay and therefore error results from different assays may be combined using a Bayesian approach to obtain estimates of assay error rates with increased precision. This paper introduces a relatively simple Bayesian approach that can be used to combine data from a present assay of interest with prior lab data on related assays to obtain updated estimates of assay error rates. If this approach is successfully implemented it can yield as much as a ten-fold reduction in required testing resources.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Remund, Kirk M. and Austin, Glenn D.
(2003).
"A BAYESIAN APPROACH TO ASSESSING LAB PROFICENCY WITH QUALITATIVE PCR ASSAYS USED TO DETECT BIOTECH TRAITS IN CROP SEED,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1183
A BAYESIAN APPROACH TO ASSESSING LAB PROFICENCY WITH QUALITATIVE PCR ASSAYS USED TO DETECT BIOTECH TRAITS IN CROP SEED
Many seed testing laboratories currently use polymerase chain reaction (PCR) assays to test conventional crop seed for the adventitous presence of biotech trait seed. Seed organizations and companies are spending much time and resources assessing laboratories proficency in running PCR assays. Since many of these assays provide qualitative rather than quantitative results, laboratories must go through a significant effort to obtain adequate assay error estimates. Many sample-processing steps are very similar from assay to assay and therefore error results from different assays may be combined using a Bayesian approach to obtain estimates of assay error rates with increased precision. This paper introduces a relatively simple Bayesian approach that can be used to combine data from a present assay of interest with prior lab data on related assays to obtain updated estimates of assay error rates. If this approach is successfully implemented it can yield as much as a ten-fold reduction in required testing resources.
