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To prevent new infections with human immunodeficiency virus type 1 (HIV-1) in sub-Saharan Africa, UNAIDS recommends targeting interventions to populations that are at high risk of acquiring and passing on the virus. Yet it is often unclear who and where these ‘source’ populations are. Here we demonstrate how viral deep-sequencing can be used to reconstruct HIV-1 transmission networks and to infer the direction of transmission in these networks. We are able to deep-sequence virus from a large population-based sample of infected individuals in Rakai District, Uganda, reconstruct partial transmission networks, and infer the direction of transmission within them at an estimated error rate of 16.3% [8.8–28.3%]. With this error rate, deep-sequence phylogenetics cannot be used against individuals in legal contexts, but is sufficiently low for population-level inferences into the sources of epidemic spread. The technique presents new opportunities for characterizing source populations and for targeting of HIV-1 prevention interventions in Africa.

Importance: Birth defects affect approximately 1 in 33 children. Some birth defects are known to be strongly associated with childhood cancer (eg, trisomy 21 and acute leukemia). However, comprehensive evaluations of childhood cancer risk in those with birth defects have been limited in previous studies by insufficient sample sizes. Objectives: To identify specific birth defect-childhood cancer (BD-CC) associations and characterize cancer risk in children by increasing number of nonchromosomal birth defects. Design, Setting, and Participants: This multistate, population-based registry linkage study pooled statewide data on births, birth defects, and cancer from Texas, Arkansas, Michigan, and North Carolina on 10181074 children born from January 1, 1992, to December 31, 2013. Children were followed up to 18 years of age for a diagnosis of cancer. Data were retrieved between September 26, 2016, and September 21, 2017, and data analysis was performed from September 2, 2017, to March 21, 2019. Exposures: Birth defects diagnoses (chromosomal anomalies and nonchromosomal birth defects) recorded by statewide, population-based birth defects registries. Main Outcomes and Measures: Cancer diagnosis before age 18 years, as recorded in state cancer registries. Cox regression models were used to generate hazard ratios (HRs) and 95% CIs to evaluate BD-CC associations and the association between number of nonchromosomal defects and cancer risk. Results: Compared with children without any birth defects, children with chromosomal anomalies were 11.6 (95% CI, 10.4-12.9) times more likely to be diagnosed with cancer, whereas children with nonchromosomal birth defects were 2.5 (95% CI, 2.4-2.6) times more likely to be diagnosed with cancer before 18 years of age. An increasing number of nonchromosomal birth defects was associated with a corresponding increase in the risk of cancer. Children with 4 or more major birth defects were 5.9 (95% CI, 5.3-6.4) times more likely to be diagnosed with cancer compared with those without a birth defect. In the analysis of 72 specific BD-CC patterns, 40 HRs were statistically significant (adjusted P

BACKGROUND: DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data. RESULTS: Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types. CONCLUSIONS: Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/.