Low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and total cholesterol are heritable, modifiable, risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,578 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5×10−8, including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian, and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipids are often associated with cardiovascular and metabolic traits including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio, and body mass index. Our results illustrate the value of genetic data from individuals of diverse ancestries and provide insights into biological mechanisms regulating blood lipids to guide future genetic, biological, and therapeutic research.

Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiologic studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P<5×10−8 for each) to examine the role of triglycerides on risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglycerides, and show that the direction and magnitude of both are factors in determining CAD risk. Second, we consider loci with only a strong magnitude of association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol, a polymorphism's strength of effect on triglycerides is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.

Given the importance of Africa to studies of human origins and disease susceptibility, detailed characterisation of African genetic diversity is needed. The African Genome Variation Project (AGVP) provides a resource to help design, implement and interpret genomic studies in sub-Saharan Africa (SSA) and worldwide. The AGVP represents dense genotypes from 1,481 and whole genome sequences (WGS) from 320 individuals across SSA. Using this resource, we find novel evidence of complex, regionally distinct hunter-gatherer and Eurasian admixture across SSA. We identify new loci under selection, including for malaria and hypertension. We show that modern imputation panels can identify association signals at highly differentiated loci across populations in SSA. Using WGS, we show further improvement in imputation accuracy supporting efforts for large-scale sequencing of diverse African haplotypes. Finally, we present an efficient genotype array design capturing common genetic variation in Africa, showing for the first time that such designs are feasible.

IntroductionThe provision of HIV treatment and care in sub-Saharan Africa faces multiple challenges, including weak health systems and attrition of trained health workers. One potential response to overcome these challenges has been to engage community health workers (CHWs).MethodologyA systematic literature search for quantitative and qualitative studies describing the role and outcomes of CHWs in HIV care between inception and December 2012 in sub-Saharan Africa was performed in the following databases: PubMed, PsychINFO, Embase, Web of Science, JSTOR, WHOLIS, Google Scholar and SAGE journals online. Bibliographies of included articles were also searched. A narrative synthesis approach was used to analyze common emerging themes on the role and outcomes of CHWs in HIV care in sub-Saharan Africa.ResultsIn total, 21 studies met the inclusion criteria, documenting a range of tasks performed by CHWs. These included patient support (counselling, home-based care, education, adherence support and livelihood support) and health service support (screening, referral and health service organization and surveillance). CHWs were reported to enhance the reach, uptake and quality of HIV services, as well as the dignity, quality of life and retention in care of people living with HIV. The presence of CHWs in clinics was reported to reduce waiting times, streamline patient flow and reduce the workload of health workers. Clinical outcomes appeared not to be compromised, with no differences in virologic failure and mortality comparing patients under community-based and those under facility-based care. Despite these benefits, CHWs faced challenges related to lack of recognition, remuneration and involvement in decision making.ConclusionsCHWs can clearly contribute to HIV services delivery and strengthen human resource capacity in sub-Saharan Africa. For their contribution to be sustained, CHWs need to be recognized, remunerated and integrated in wider health systems. Further research focusing on comparative costs of CHW interventions and successful models for mainstreaming CHWs into wider health systems is needed.

Background Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex-workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total-population data. Methods Total-population data on age, tribe, religion, socioeconomic status, sexual activity and HIV status were available on a population of 2402 male household-heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, employing current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample). Results We recruited 927 household-heads. Full and small RDS samples were largely representative of the total population, but both samples under-represented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven-sampling statistical-inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven-sampling bootstrap 95% confidence intervals included the population proportion. Conclusions Respondent-driven sampling produced a generally representative sample of this well-connected non-hidden population. However, current respondent-driven-sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience-sampling method, and caution is required when interpreting findings based on the sampling method.

Highlights d The Uganda Genome Resource comprises genetic and phenotypic data on 6,400 individuals d Ugandans show geographically correlated genetic substructure and complex admixture d The Uganda sequence panel substantially improves imputation in African populations d The Uganda Genome Resource enables novel discovery of loci associated with traits

These fishing communities experienced high HIV infection, which was mainly explained by high-risk behavior. There is an urgent need to target HIV prevention and research efforts to this vulnerable and neglected group.

This fishing population characterised by a very high HIV prevalence, high syphilis prevalence and frequently reported sexual risk behaviours, urgently needs improved STI services and targeted behavioural interventions.