title: Estimating true human and animal host source contribution in quantitative microbial source tracking using the Monte Carlo method.
abstract: Cultivation- and library-independent, quantitative PCR-based methods have become the method of choice in microbial source tracking. However, these qPCR assays are not 100% specific and sensitive for the target sequence in their respective hosts' genome. The factors that can lead to false positive and false negative information in qPCR results are well defined. It is highly desirable to have a way of removing such false information to estimate the true concentration of host-specific genetic markers and help guide the interpretation of environmental monitoring studies. Here we propose a statistical model based on the Law of Total Probability to predict the true concentration of these markers. The distributions of the probabilities of obtaining false information are estimated from representative fecal samples of known origin. Measurement error is derived from the sample precision error of replicated qPCR reactions. Then, the Monte Carlo method is applied to sample from these distributions of probabilities and measurement error. The set of equations given by the Law of Total Probability allows one to calculate the distribution of true concentrations, from which their expected value, confidence interval and other statistical characteristics can be easily evaluated. The output distributions of predicted true concentrations can then be used as input to watershed-wide total maximum daily load determinations, quantitative microbial risk assessment and other environmental models. This model was validated by both statistical simulations and real world samples. It was able to correct the intrinsic false information associated with qPCR assays and output the distribution of true concentrations of Bacteroidales for each animal host group. Model performance was strongly affected by the precision error. It could perform reliably and precisely when the standard deviation of the precision error was small (≤ 0.1). Further improvement on the precision of sample processing and qPCR reaction would greatly improve the performance of the model. This methodology, built upon Bacteroidales assays, is readily transferable to any other microbial source indicator where a universal assay for fecal sources of that indicator exists.
authors.affiliation: Department of Civil & Environmental Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA.
authors.forename: Dan
authors.initials: D
authors.lastname: Wang
authors.forename: Sarah S
authors.initials: SS
authors.lastname: Silkie
authors.forename: Kara L
authors.initials: KL
authors.lastname: Nelson
authors.forename: Stefan
authors.initials: S
authors.lastname: Wuertz
mesh_terms: D000818:Animals; D041963:Bacteroidetes / Q000235:genetics; D001681:Biological Assay; D004784:Environmental Monitoring / Q000379:methods*; D005243:Feces / Q000382:microbiology*; D005819:Genetic Markers / Q000235:genetics; D006801:Humans; D009010:Monte Carlo Method; D016133:Polymerase Chain Reaction; D011336:Probability; D018570:Risk Assessment; D014871:Water Microbiology; D014876:Water Pollution / Q000032:analysis*
chemical_list: D005819:Genetic Markers