BackgroundIn low tuberculosis (TB) incidence countries, contact investigation (CI) requires not missing contacts with TB infection or disease without unnecessarily evaluating non-infected contacts.AimWe assessed whether updated guidelines for the stone-in-the-pond principle and their promotion improved CI practices.MethodsThis retrospective study used surveillance data to compare CI outcomes before (2011-2013) and after (2014-2016) the guideline update and promotion. Using negative binomial regression and logistic regression models, we compared the number of contacts invited for CI per index patient, the number of CI scaled-up according to the stone-in-the-pond principle, the TB and latent TB infection (LTBI) testing coverage, and yield.ResultsPre and post update, 1,703 and 1,489 index patients were reported, 27,187 and 21,056 contacts were eligible for CI, 86% and 89% were tested for TB, and 0.70% and 0.73% were identified with active TB, respectively. Post update, the number of casual contacts invited per index patient decreased statistically significantly (RR = 0.88; 95% CI: 0.79-0.98), TB testing coverage increased (OR = 1.4; 95% CI: 1.2-1.7), and TB yield increased (OR = 2.0; 95% CI: 1.0-3.9). The total LTBI yield increased from 8.8% to 9.8%, with statistically significant increases for casual (OR = 1.2; 95% CI: 1.0-1.5) and community contacts (OR = 2.0; 95% CI: 1.6-3.2). The proportion of CIs appropriately scaled-up to community contacts increased statistically significantly (RR = 1.8; 95% CI: 1.3-2.6).ConclusionThis study shows that promoting evidence-based CI guidelines strengthen the efficiency of CIs without jeopardising effectiveness. These findings support CI is an effective TB elimination intervention.

BACKGROUND: Many large-effect quantitative trait loci (QTLs) for yield and disease resistance related traits have been identified in different mapping populations of peanut (Arachis hypogaea L.) under multiple environments. However, only a limited number of QTLs have been used in marker-assisted selection (MAS) because of unfavorable epistatic interactions between QTLs in different genetic backgrounds. Thus, it is essential to identify consensus QTLs across different environments and genetic backgrounds for use in MAS. Here, we used QTL meta-analysis to identify a set of consensus QTLs for yield and disease resistance related traits in peanut.
RESULTS: A new integrated consensus genetic map with 5874 loci was constructed. The map comprised 20 linkage groups (LGs) and was up to a total length of 2918.62 cM with average marker density of 2.01 loci per centimorgan (cM). A total of 292 initial QTLs were projected on the new consensus map, and 40 meta-QTLs (MQTLs) for yield and disease resistance related traits were detected on four LGs. The genetic intervals of these consensus MQTLs varied from 0.20 cM to 7.4 cM, which is narrower than the genetic intervals of the initial QTLs, meaning they may be suitable for use in MAS. Importantly, a region of the map that previously co-localized multiple major QTLs for pod traits was narrowed from 3.7 cM to 0.7 cM using an overlap region of four MQTLs for yield related traits on LG A05, which corresponds to a physical region of about 630.3 kb on the A05 pseudomolecule of peanut, including 38 annotated candidate genes (54 transcripts) related to catalytic activity and metabolic process. Additionally, one major MQTL for late leaf spot (LLS) was identified in a region of about 0.38 cM. BLAST searches identified 26 candidate genes (30 different transcripts) in this region, some of which were annotated as related to regulation of disease resistance in different plant species.
CONCLUSIONS: Combined with the high-density marker consensus map, all the detected MQTLs could be useful in MAS. The biological functions of the 64 candidate genes should be validated to unravel the molecular mechanisms of yield and disease resistance in peanut.