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Rao, D.

Publications and source records attributed to Rao, D..

2 recordsLinked to original sources

The PrEP Care Continuum and Racial Disparities in HIV Incidence among Men Who Have Sex with Men

The potential for HIV preexposure prophylaxis (PrEP) to reduce the racial disparities in HIV incidence in the United States may be limited by racial gaps in PrEP care. We used a network-based mathematical model of HIV transmission for younger black and white men who have sex with men (B/WMSM) in the Atlanta area to evaluate how race-stratified transitions through the PrEP care continuum from initiation to adherence and retention could impact HIV incidence overall and disparities in incidence between races, using current empirical estimates of BMSM continuum parameters. Relative to a no-PrEP scenario, implementing PrEP according to observed BMSM parameters was projected to yield a 23% decline in HIV incidence (HR = 0.77) among BMSM at year 10. The racial disparity in incidence in this observed scenario was 4.95 per 100 person-years at risk (PYAR), a 19% decline from the 6.08 per 100 PYAR disparity in the no-PrEP scenario. If BMSM parameters were increased to WMSM values, incidence would decline by 47% (HR = 0.53), with an associated disparity of 3.30 per 100 PYAR (a 46% decline in the disparity). PrEP could simultaneously lower HIV incidence overall and reduce racial disparities despite current gaps in PrEP care. Interventions addressing these gaps will be needed to substantially decrease disparities.

epidemiology

VarExp: Estimating variance explained by Genome-Wide GxE summary statistics

Many genomic analyses, such as genome-wide association studies (GWAS) or genome-wide screening for Gene-Environment (GxE) interactions have been performed to elucidate the underlying mechanisms of human traits and diseases. When the analyzed outcome is quantitative, the overall contribution of identified genetic variants to the outcome is often expressed as the percentage of phenotypic variance explained. In practice, this is commonly estimated using individual genotype data. However, using individual-level data faces practical and ethical challenges when the GWAS results are derived in large consortia through meta-analysis of results from multiple cohorts. In this work, we present a R package, \"VarExp\", that allows for the estimation of the percentage of phenotypic variance explained by variants of interest using summary statistics only. Our package allows for a range of models to be evaluated, including marginal genetic effects, GxE interaction effects, and main genetic and interaction effects jointly. Its implementation integrates all recent methodological developments on the topic and does not need external data to be uploaded by users.\n\nThe R source code, tutorial and associated example are available at https://gitlab.pasteur.fr/statistical-genetics/VarExp.git.

bioinformatics