Peer review in Large-scale whole genome sequencing of M. tuberculosis provides insights into transmission in a high prevalence area

Decision letter
Author response

Decision letter

Quarraisha Abdool Karim

Reviewing Editor; University of KwaZulu Natal, South Africa

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Large scale whole genome sequencing of M. tuberculosis provides insights into transmission in a high prevalence area” for consideration at eLife. Your article has been favorably evaluated by Prabhat Jha (Senior editor), a Reviewing editor, and two reviewers.

The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

Kindly elaborate the analysis plan in relation to unraveling the transmission clusters, e.g. were there a priori hypothesis and criteria set out or was this determined in an ad hoc manner? How are epidemiological transmission clusters related to the clusters from the RFLP analysis?

Reviewer #1:

This manuscript reports the results of a large-scale whole genome sequencing analysis of M. tuberculosis isolates obtained in a population-based study in Malawi spanning 15 years. The study is unique in its scope and size. The analyses provide important insights in transmission dynamics, in particular in relation to major lineages. The manuscript is well written, the analyses are sound and the tables and figures are clear. In summary, an important contribution to the subject that deserves publication provided that some (overall rather minor) concerns are addressed.

From Figure 2 it seems that there are different individuals with isolates within the same RFLP that have up to 50 SNPs difference. Authors took a rather arbitrary cut-off for distinguishing links of up to 10 SNPs difference. It would be important to see to what extent their main findings are sensitive to this choice of cut-off, in particular the associations with, and differences in mutation rates for lineages. This could be done by repeating the main analyses at a higher cut-off.

In the Methods section: “We estimated the between-patient mutation rate (…) between disease onset dates (…)”. How were disease onset dates ascertained? Were diagnostic delays estimated and if so, how? How robust are these estimates? I would suspect that dates of onset are associated with date of diagnosis with certain elasticity, as will be the dates of specimen collection. Therefore predictor estimates may be confounded by determinants of (reported) delay. I am not sure that the multivariable analyses take this confounding sufficiently into account, and authors should acknowledge this potential shortcoming and its consequences for their conclusions in the Discussion section.

Reviewer #2:

The authors should be commended for this excellent study which describes the largest collection of whole genome sequences of M. tuberculosis isolates assembled to date. The central aim of the study was to use whole genome sequences to construct a transmission network of cases resident in a high prevalence setting. This is a challenging exercise as the complexity of transmission networks increases with incidence. The authors also used their data to estimate the evolutionary rate of the M. tuberculosis genome and the proportion of recent transmission events. This study extends the current knowledge of transmission and genetic variability occurring in M. tuberculosis isolates from a high incidence setting.

The hypothesis that genetic distance is a measure of transmission is not validated within this setting: the authors should provide additional data to support the notion that isolates differing by less than 10 SNPs reflect transmission.

https://doi.org/10.7554/eLife.05166.014

Author response

Kindly elaborate the analysis plan in relation to unraveling the transmission clusters, e.g. were there a priori hypothesis and criteria set out or was this determined in an ad hoc manner? How are epidemiological transmission clusters related to the clusters from the RFLP analysis?

We have addressed these points below, where they are raised by the reviewers.

Full major concerns:

Reviewer #1:

From Figure 2 it seems that there are different individuals with isolates within the same RFLP that have up to 50 SNPs difference. Authors took a rather arbitrary cut-off for distinguishing links of up to 10 SNPs difference. It would be important to see to what extent their main findings are sensitive to this choice of cut-off, in particular the associations with, and differences in mutation rates for lineages. This could be done by repeating the main analyses at a higher cut-off.

We have added further justification for our cut-off to the Methods section: “We have previously shown in 92 patients in this dataset with repeat samples from the same or different episodes of disease that, using the same pipeline, there is a clear bimodal distribution, with pairs of samples either having up to 8 SNPs between them or more than 100 SNPs (Guerra-Assuncao et al., 2014). Furthermore, among 170 pairs of individuals with epidemiological links, 62 had ≤10 SNPs, 9 had 10-99 SNPs and 116 had ≥100 SNPs.”

Other studies have used a similar or stricter cut-off. To look for associations with transmission we were keen to have a cut-off with high specificity.

We have clarified that we used the date of first evidence of tuberculosis. (We have not attempted to estimate diagnostic delay). We have added this to the Discussion.

Reviewer #2:

See our response above to reviewer 1. We have now added further data to justify this decision, based on repeat specimens from individuals and on comparisons between individuals with known epidemiological links.

https://doi.org/10.7554/eLife.05166.015