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Principles of Molecular Epidemiology Lecture 4, Part 1: Validation of new laboratory techniques applied to molecular epidemiology studies National Institute of Infectious Disease January 16, 2017 Learning objectives  Define validity of a new strain-typing test used for epidemiologic investigation  Describe the steps used to validate a new test to investigate transmission of infectious diseases that occur as an outbreak  Describe the steps used to validate a new test to investigate transmission of infectious diseases that are not recognized to occur as an outbreak Validity of a new test: Validity: ability of a test to correctly predict or identify those who truly have the characteristic the test is trying to detect, and exclude those who not have the characteristic • Sensitivity • Specificity Validity is determined by comparison of the observed results to a reference standard, “truth”, “gold standard” • In molecular epidemiology, a new test is validated by its ability to discriminate strains that are epidemiologically related from those that are not In molecular epidemiology, validity is determined empirically Steps used to validate a new molecular strain typing test for epidemiologic investigation of infectious diseases Assess whether or not the infectious disease in question occurs as an outbreak e.g., Salmonellosis versus urinary tract infections If it occurs as outbreak: Demonstrate that the typing information generated by the test is indistinguishable for all isolates from persons with disease in a recognized outbreak Select appropriate comparison isolates (geographic and temporal controls), and show that the typing information from these isolates is distinct from that of the outbreak isolates Steps used to validate a new test in molecular epidemiology— cont Occurs as outbreak, cont.: • Ascertain fidelity of the typing information used • temporal stability of the taxonomic unit; • clonality of the isolates obtained from a single host) • Perform new analysis in • a prospective study • endemic settings assess whether or not the analyses based on the test yields epidemiologically identical or similar association found in the outbreak investigation Steps used to validate a new test in molecular epidemiology—cont If outbreak occurrence is uncertain: Demonstrate whether or not the test identifies seemingly unrelated isolates to belong to distinct clonal groups Assess whether or not the analyses based on the test yields any epidemiologically useful or meaningful relationship among persons infected with the clonal group strains Show that isolates that not belong to the clonal group not have the same epidemiologic association Ascertain fidelity of the typing information If possible, eliminate the identified or putative risk factor and evaluate if this will lead to control or amelioration of the problem Final test of validity of a molecular typing technique: For molecular epidemiology, a strain typing test that cannot yield any epidemiologically useful or meaningful information, no matter how simple, discriminating, or taxonomically relevant, is not valid! Principles of Molecular Epidemiology Lecture 4, Part 2: Analysis of similarity and relatedness National Institute of Infectious Disease January 16, 2017 Learning objectives  Be able to describe the concept of probability errors applied to strain typing      methods used in epidemiologic investigations Define type and type probability errors as used in molecular epidemiology Describe cladistic vs phenetic methods of classifying microbes Understand the appropriate applications of similarity coefficient calculations to analyze patterns generated from strain-typing methods Describe different ways to measure reliability of the relationships portrayed by a dendrogram Name different analytical tools needed to conduct molecular epidemiologic investigations What can you with this pattern? Phylogenetic tree of E coli O157:H7 by their core genes (Kaas RS et al, 2012) “What we observe is not nature itself, but nature exposed to our method of questioning.” Assessing reliability of relatedness measures—cont  Their “true” relationship needs to be empirically determined In some situations, this can be done (e.g., outbreaks) If this cannot be done (e.g., relationship of multiple nucleic acid sequences—alignments), the data points need to be examined for their reliability by a stochastic method Measures of reliability of data points  Resampling methods:  Bootstrapping  Data points from the original data set containing n data points are randomly and repeatedly sampled until new sample sets, each containing n points are created  Jackknifing  Data points from the original data set containing n points are resampled by dropping x data points each time, and the variance is estimated from n-x data points in the remaining sample Example of resampling of nucleotide sequences—bootstrapping: OTU A: 5’-atgggcgacttcatcacgatgaggtcaggaggccactatt ref B: 5’-atgggctacttcttcacgatcaggtcaggaggccactatt C: 5’-atcggcgacttcatcacgatgaggtgtggaggccactatt D: 5’-aagggcgacttcatcaccatgaggtcaggaggccactata E: 5’-atgggcgattttaccactttgaggtcaggtggccggtatt F: 5’-atggcttgctttataacgattaggtgagaaggccactatt G: 5’-cagggcgacttcatcttagcctggtcagcaggccacgatt OTU A B 93 C 93 85 D 93 85 85 E 80 73 73 75 F 80 80 78 73 65 G 73 70 65 73 58 B C D E F 58 Dendrogram generated from the original data set 1 1 02 1 1 1 1 1 2 11 2 1 Number of times each column (data point) is resampled A: B: C: D: E: F: G: 5’-atgggcgacttcatcacgatgaggtcaggaggccactatt 5’-atgggctacttcttcacgatcaggtcaggaggccactatt 5’-atcggcgacttcatcacgatgaggtgtggaggccactatt 5’-aagggcgacttcatcaccatgaggtcaggaggccactata original set 5’-atgggcgattttaccactttgaggtcaggtggccggtatt 5’-atggcttgctttataacgattaggtgagaaggccactatt 5’-cagggcgacttcatcttagcctggtcagcaggccacgatt A: B: C: D: E: F: G: 5’-tggggggcttcttccgtggagtcagagggcccacctaatt 5’-tgggggtcttcttccgtccagtcagagggcccacctaatt 5’-tggggggcttcttccgtggagtgtgagggcccacctaatt 5’-aggggggcttcttccctggagtcagagggcccacctaata 5’-tggggggttttccccttggagtcagtgggcccgggtaatt 5’-tggccctctttttacgtttagtgagagggcccacctaatt 5’-aggggggcttcttctaccctgtcagagggcccaccgaatt resampled set original resampled Resampling methods—cont  Degree of deviation from the original tree among the pseudosamples measures the reliability of the original tree If there is no deviation, then the original tree can be said to be unaffected by any stochastic effects Typical software requirements in a laboratory for database analysis involving a molecular epidemiology project Study design methods power, sample size calculation questionnaire design data entry, storage, line listing data analysis Software EpiInfo EpiInfo EpiInfo, Access., Excel, etc EpiInfo Advanced statistical methods EpiInfo, STATA, SAS, SPSS, R Capturing and storing pattern images tiff, jpeg, gif, etc Image normalization, similarity/distance and cluster analysis, storage, tree generation GelCompar, Molecular Analyst Sequence alignment ClustalX Bootstrapping MEGA Command-line search programs used to analyze high-throughput sequences  Nucleic acid sequence     assembly to create contigs Ordering contigs  Against reference seq  De novo Annotation Comparative analysis, pairwise and multiple sequence alignment Specific gene analysis  Velvet  Mauve, ABACAS, Artemis Comparison Tool (ACT)  RAST  Mauve (multiple), ACT (pair-wise), BRIG (reference comparison)  RESfinder (resistance genes), PHAST (phage genes)