Biology subjects

Rao, D.

Publications and source records attributed to Rao, D..

24 records · Page 2Linked to original sources

bioRxiv2019

Deriving stratified effects from joint models investigating Gene-Environment Interactions

BackgroundModels including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models.\n\nResultsIn this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the \"joint\" model. We performed simulation studies to assess our estimators accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators retained accuracy after filtering SNPs by sample size to mitigate potential bias.\n\nConclusionsThese analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/J2S.

bioinformatics↗

bioRxiv2019

Genome-wide Interaction Studies by the CHARGE Gene-Lifestyle Interactions Working Group: what we have learned and what is coming next.

The CHARGE Gene-Lifestyle Interactions Working Group is a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

genetics↗

bioRxiv2019

HBEGF+ macrophages identified in rheumatoid arthritis promote joint tissue invasiveness and are reshaped differentially by medications

Macrophages tailor their function to the signals found in tissue microenvironments, taking on a wide spectrum of phenotypes. In human tissues, a detailed understanding of macrophage phenotypes is limited. Using single-cell RNA-sequencing, we define distinct macrophage subsets in the joints of patients with the autoimmune disease rheumatoid arthritis (RA), which affects ~1% of the population. The subset we refer to as HBEGF+ inflammatory macrophages is enriched in RA tissues and shaped by resident fibroblasts and the cytokine TNF. These macrophages promote fibroblast invasiveness in an EGF receptor dependent manner, indicating that inflammatory intercellular crosstalk reshapes both cell types and contributes to fibroblast-mediated joint destruction. In an ex vivo tissue assay, the HBEGF+ inflammatory macrophage is targeted by several anti-inflammatory RA medications, however, COX inhibition redirects it towards a different inflammatory phenotype that is also expected to perpetuate pathology. These data highlight advances in understanding the pathophysiology and drug mechanisms in chronic inflammatory disorders can be achieved by focusing on macrophage phenotypes in the context of complex interactions in human tissues. One Sentence SummaryA newly identified human macrophage phenotype from patients with the autoimmune condition RA is found to promote joint tissue invasiveness and demonstrates variable sensitivities to anti-inflammatory medications used to treat the disease.

immunology↗

bioRxiv2019

Sensory feedback independent pre-song vocalizations correlate with current state of motor preparation

Many self-initiated, learned, motor sequences begin by repeating a simple movement, like ball-bouncing before a tennis serve, and this repetition is thought to represent motor preparation. Do these simple movements provide real-time sensory feedback used by the brain for getting ready or do they simply reflect internal neural preparatory processes? Here, we addressed this question by examining the introductory notes (INs) that zebra finches repeat before starting their learned song sequence. INs progress from a variable initial state to a stereotyped final state before each song and are thought to represent motor preparation before song. Here, we found that the mean number of INs before song and the progression of INs to song were not affected by removal of two sensory feedback pathways (auditory and proprioceptive). In both feedback-intact and feedback-deprived birds, the presence of calls (other non-song vocalizations), just before the first IN, was correlated with fewer INs before song and an initial state closer to song. Finally, the initial IN state correlated with the time to song initiation. Overall, these results show that INs do not provide real-time sensory feedback for preparing the motor system. Rather, repetition of INs, and possibly, other such simple movements, may reflect the \"current\" state of internal neural preparatory processes involved in getting the brain ready to initiate a learned movement sequence.\n\nSUMMARY STATEMENTThe number and progression of introductory notes to song in the zebra finch are not affected by removal of sensory feedback.

neuroscience↗

bioRxiv2018

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↗

bioRxiv2017

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↗