Biology subjects

Morrison, A. C.

Publications and source records attributed to Morrison, A. C..

18 recordsLinked to original sources

bioRxiv2026

ScreenDMT reveals metabolites replicably associated with sleep disorders across three human cohorts

The robust replication of scientific findings across independent studies is essential, yet it remains challenging in high-dimensional biological datasets. A critical aspect of true replication requires detecting not only the repeated presence but also the consistent direction of an effect. Traditional methods for evaluating replication often ignore effect directionality or lack adequate statistical power when adjusting for multiple comparisons. To address this, we extend the directional MaxP test (DMT) and our screening-integrated procedure, ScreenDMT, to evaluate directional replication across an arbitrary number of independent studies. We mathematically prove ScreenDMT's validity for an arbitrary number of studies when the studies' sample sizes grow unbounded. Through extensive simulation studies, we demonstrate that ScreenDMT effectively controls error rates, such as the false discovery rate (FDR), in finite sample sizes and yields higher statistical power than competing methods especially for three studies. We applied the extended ScreenDMT to untargeted blood metabolomics profiles across three diverse human cohorts - ARIC (N=1,337), MESA (N=1,874), and HCHS/SOL (N=6,180) - to discover replicable biomarkers for dichotomous insomnia, long sleep, short sleep, and excessive daytime sleepiness (EDS). We found low levels of cortisol to be replicably associated with EDS, and high levels of n-formylmethionine to be associated with long sleep. Furthermore, directional pathway analysis revealed that low levels of xanthine metabolites are replicably associated with insomnia. These convergent findings validate ScreenDMT as a powerful methodology for simultaneous multi-cohort replication and offer robust insights into the metabolic signatures underlying sleep disorders across diverse populations.

bioinformatics↗

bioRxiv2026

Efficacy of an attractive lethal ovitrap to reduce populations of Aedes mosquitoes: a controlled trial in Iquitos, Peru

IntroductionDengue, one of the most important arboviral infections worldwide, is transmitted primarily by the mosquito Aedes aegypti, a vector closely associated with human habitations. Because vector control is the primary prevention strategy for this disease novel tools are urgently needed. We evaluated an Attractive Lethal OviTrap (ALOT) that targets epidemiologically relevant gravid female mosquitoes for public health impact against dengue disease. MethodsWe conducted a proof-of-concept field efficacy trial in the Amazonian city of Iquitos, Peru, to quantify the reduction of vector density, parity, and sex ratio and symptomatic human dengue infection over a 3.5 year follow-up period. After three baseline entomological surveys in core (753 houses), and buffer (1,549 houses) areas where traps were placed in and around homes, and a control (1,233 houses) area without traps, entomological surveys were carried out every 2 months, and household residents were monitored for dengue disease 3 times a week. Trap maintenance was conducted at 2-3 week intervals by study staff during the first 2.5 years and by residents in the final year. ResultsTotal and female Ae. aegypti abundance demonstrated a strong initial effect with 52% and 47% fewer mosquitoes after trap placement, respectively (Total: RR = 0.48, 95% CI: 0.40 - 0.58, p < 0.001; Females: RR = 0.53, 95% CI: 0.43 - 0.66, p < 0.001). The effect diminished significantly, however, over the study period (interaction coefficient = 0.035 per month, p < 0.001; interaction coefficient = 0.030 per month, p < 0.001). Female-to-male ratio decreased after trap deployment and impact on non-Aedes abundance was like that of Aedes. Cumulative symptomatic dengue incidence was 1.40% (95% CI: 1.00%-1.70%) in the ALOT area versus 3.20% (95% CI: 2.60%-3.80%) in the control area, a 56% relative reduction (log-rank {chi}{superscript 2}=30.0, p<0.001). Restricted mean survival time analysis showed participants in the intervention area on average remained dengue-free for 15.5 additional days (95% CI: 11.1-20.0, p<0.001). ConclusionsThe ALOT strategy functioned as predicted, decreasing but not eliminating female vector densities, with a clear public health impact, lowering dengue disease in areas where traps were deployed. Our study also illustrates the challenges associated with conducting large-scale vector control trials and the need to consider programmatic implementation early in the process of bringing a new product to market. Author SummaryDengue is one of the most important mosquito transmitted infections worldwide. Aedes aegypti, the most important species involved, has a unique biology living in containers associated with households. New methods to control this mosquito are needed and we present our evaluation of a novel trap called the Attractive Lethal OviTrap (ALOT) that targets older female mosquitoes that are the most important for dengue disease transmission. We conducted a trial in the Amazonian city of Iquitos, Peru, to measure reductions in mosquito numbers, age, and sex ratio and symptomatic human dengue infections over a 3.5 years. After baseline entomological surveys prior to placing ALOT traps in homes in a treatment area, which included a central core area with 753 houses and a protective buffer area with 1,549 houses areas. We also had a control area without any traps in 1,233 houses. Mosquito surveys were carried out every 2 months, and household residents were monitored for dengue disease 3 times per week. Trap maintenance was conducted at 2 - 3 week intervals by study staff during the first 2.5 years and by residents in the final year. The number of all Ae. aegypti were initially reduced by 52% after trap placement whereas females were reduced by 47%. The effect diminished significantly, however, over the study period. Female-to-male ratio decreased after trap deployment and impact on other mosquito species was like that of Aedes. Dengue incidence was 56% less in the area with traps compared to the control area, estimating that participants in the intervention area on average remained dengue-free for 15.5 additional days. The ALOT strategy functioned as predicted, decreasing but not eliminating female mosquitos and had a clear public health impact, lowering dengue disease in areas where traps were deployed. Our study also illustrates the challenges associated with conducting large-scale vector control trials and the need to consider programmatic implementation early in the process of bringing a new product to market.

ecology↗

bioRxiv2026

Direct and Indirect Entomological Efficacy of Targeted Indoor Residual Spraying against Aedes aegypti in Iquitos, Peru

BackgroundControl of Aedes aegypti, the primary vector of dengue and other Aedes-borne viruses, is challenged by insecticide resistance, limited efficacy of existing tools and the large and widespread epidemics. Targeted Indoor Residual Spraying (TIRS), a modification of traditional indoor residual spraying focused on Ae. aegypti resting sites, has demonstrated promising results, yet its indirect community-wide effects remain underexplored. Methodology/Principal FindingsWe conducted an entomological cluster-randomized controlled trial in Iquitos, Peru, to evaluate the direct and indirect entomological impacts of TIRS using pirimiphos-methyl. Thirty clusters were randomized to receive either TIRS (15 clusters, 898 structures) or standard Ministry of Health vector control activities (15 clusters, 1,018 structures). Aedes aegypti indoor densities were assessed in the 45 days pre-intervention and at four time points up to 255 days post-intervention using Prokopack aspiration. Generalized linear mixed models with a negative binomial link were used to estimate incidence rate ratios (IRRs) and calculate efficacy (1-IRR) for houses that received TIRS (direct effect) and untreated houses in TIRS clusters (indirect effect). Direct efficacy reached 96% at 15 days post-spraying and remained significant (40%) at 255 days post-spraying. Indirect efficacy reached 69% at 15 days and declined to 7% by 255 days post-spraying. Despite only 57% household-level TIRS coverage, both direct and indirect impacts on Ae. aegypti were significant during early post-intervention surveys, and after 8 months in TIRS clusters. Conclusions/SignificanceTIRS provided substantial and sustained reductions in indoor Ae. aegypti density, including measurable indirect effects in untreated homes within intervention clusters. These findings demonstrate the entomological value of TIRS even at moderate coverage levels and highlight its potential for both preventive and reactive vector control programs and should be considered for implementation by Ministries of Health in dengue-endemic urban settings as well as by the U.S. military when deployed to tropical or subtropical locations.

ecology↗

bioRxiv2024

Changing dynamics of Aedes aegypti invasion and vector-borne disease risk for rural communities in the Peruvian Amazon

Aedes aegypti, the primary vector of dengue virus, is predominantly considered an urban mosquito, especially in the Americas, where its reemergence began in cities after the end of continent-wide eradication campaigns. The results of our study diverge from this narrative, demonstrating the recent and widespread rural invasion of Ae. aegypti along major shipping routes in the northern Peruvian Amazon between the major cities of Iquitos, Pucallpa, and Yurimaguas. We identified Ae. aegypti populations in 29 of 30 sites surveyed across a rural to urban gradient and characterized mosquito larval habitats and Ae. aegypti adult metrics. Households, regardless of community size, were more likely to be positive for Ae. aegypti adult(s) and had a higher number of Ae. aegypti adults if a mosquito larval habitat was identified on the property, compared to houses without a larval habitat. In multiple instances, adult Ae. aegypti indices in rural villages were equal to or greater than indices in dengue-endemic cities, suggesting the entomological risk level in some rural areas is sufficient to sustain dengue transmission. Fourteen rural sites were sampled in transects from the community river port into town. In seven of these sites, houses closer to the port were significantly more likely to be infested with Ae. aegypti adults than houses further from the ports, and four additional sites had a marginal relationship to the same effect. This pattern suggests that many rural sites are invaded via adult Ae. aegypti disembarking from boats at the port, finding nearby oviposition sites, and advancing stepwise into town. The existence of the pattern also suggests that many of the sites are still experiencing active invasion, with sections of towns still Ae. aegypti-free. Only one site shows a strong signal of invasion via the egg or larval stage, with a focus of Ae. aegypti far removed from the port. The widespread infestation of Ae. aegypti in rural areas is a major public health threat given the far distance of communities to hospital care. It is important to implement control measures now before the mosquito gains a stronger foothold in zones of active invasion.

ecology↗

bioRxiv2022

Fusing an agent-based model of mosquito population dynamics with a statistical reconstruction of spatio-temporal abundance patterns

The mosquito Aedes aegypti is the vector of a number of medically-important viruses, including dengue virus, yellow virus, chikungunya virus, and Zika virus, and as such vector control is a key approach to managing the diseases they cause. Understanding the impact of vector control on these diseases is aided by first understanding its impact on Ae. aegypti population dynamics. A number of detail-rich models have been developed to couple the dynamics of the immature and adult stages of Ae. aegypti. The numerous assumptions of these models enable them to realistically characterize impacts of mosquito control, but they also constrain the ability of such models to reproduce empirical patterns that do not conform to the models behavior. In contrast, statistical models afford sufficient flexibility to extract nuanced signals from noisy data, yet they have limited ability to make predictions about impacts of mosquito control on disease caused by pathogens that the mosquitoes transmit without extensive data on mosquitoes and disease. Here, we demonstrate how the differing strengths of mechanistic realism and statistical flexibility can be fused into a single model. Our analysis utilizes data from 176,352 household-level Ae. aegypti aspirator collections conducted during 1999-2011 in Iquitos, Peru. The key step in our approach is to calibrate a single parameter of the model to spatio-temporal abundance patterns predicted by a generalized additive model (GAM). In effect, this calibrated parameter absorbs residual variation in the abundance time-series not captured by other features of the mechanistic model. We then used this calibrated parameter and the literature-derived parameters in the agent-based model to explore Ae. aegypti population dynamics and the impact of insecticide spraying to kill adult mosquitoes. The baseline abundance predicted by the agent-based model closely matched that predicted by the GAM. Following spraying, the agent-based model predicted that mosquito abundance rebounds within about two months, commensurate with recent experimental data from Iquitos. Our approach was able to accurately reproduce abundance patterns in Iquitos and produce a realistic response to adulticide spraying, while retaining sufficient flexibility to be applied across a range of settings. Author SummaryThe mosquito Aedes aegypti is the vector for a number of the most medically important viruses, including dengue, Zika, chikungunya, and yellow fever. Understanding the population dynamics of this mosquito, and how those dynamics might respond to vector control interventions, is critical to inform the deployment of such interventions. One of the best ways to gain this understanding is through modeling of population dynamics. Such models are often categorizes as either statistical or dynamical, and each of these approaches has advantages and disadvantages - for instance, statistical models may more closely match patterns observed in empirical data, while dynamical models are better able to predict the impact of counterfactual situations such as vector control strategies. In this paper, we present an approach which fuses these two approaches in order to gain the advantages of both: it fits empirical data on Aedes aegypti population dynamics well, while producing realistic responses to vector control interventions. Our approach has the potential to inform and improve the deployment of vector control interventions, and, when used in concert with and epidemiological model, to help reduce the burden of the diseases spread by such vectors.

ecology↗

bioRxiv2021

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

genetics↗

bioRxiv2021

A critical assessment of the detailed Aedes aegypti simulation model Skeeter Buster 2 using field experiments of indoor insecticidal control in Iquitos, Peru

The importance of mosquitoes in human pathogen transmission has motivated major research efforts into mosquito biology in pursuit of more effective vector control measures. Aedes aegypti is a particular concern in tropical urban areas, where it is the primary vector of numerous flaviviruses, including the yellow fever, Zika, and dengue viruses. With an anthropophilic habit, Ae. aegypti prefers houses, human blood meals, and ovipositioning in water-filled containers. We hypothesized that this relatively simple ecological niche should allow us to predict the impacts of insecticidal control measures on mosquito populations. To do this, we use Skeeter Buster 2 (SB2), a stochastic, spatially explicit, mechanistic model of Ae. aegypti population biology. SB2 builds on Skeeter Buster, which reproduced equilibrium dynamics of Ae. aegypti in Iquitos, Peru. Our goal was to predict the response of mosquito populations to perturbations by indoor insecticidal spraying and widespread destructive insect surveys. To evaluate SB2, we conducted two field experiments in Iquitos, Peru: a smaller pilot study in 2013 (S-2013) followed by a larger experiment in 2014 (L-2014). Here, we compare model predictions with (pre-viously reported) empirical results from these experiments. In both simulated and empirical populations, repeated spraying yielded substantial yet temporary reductions in adult densities. The proportional effects of spraying were broadly comparable between simulated and empirical results, but we found noteworthy differences. In particular, SB2 consistently over-estimated the proportion of nulliparous females and the proportion of containers holding immature mosquitoes. We also observed less temporal variation in simulated surveys of adult abundance relative to corresponding empirical observations. Our results indicate the presence of ecological heterogeneities or sampling processes not effectively represented by SB2. Although additional empirical research could further improve the accuracy and precision of SB2, our results under-score the importance of non-linear dynamics in the response of Ae. aegypti populations to perturbations, and suggest general limits to the fine-grained predictability of its population dynamics over space and time.

ecology↗

bioRxiv2021

Evolution of Knockdown Resistance Haplotypes in Response to Pyrethroid Selection in Aedes aegypti

This study describes the evolution of knockdown resistance (kdr) haplotypes in Aedes aegypti in response to pyrethroid insecticide use over the course of 18 years in Iquitos, Peru. Based on the duration and intensiveness of sampling ([~]10,000 samples), this is the most thorough study of kdr population genetics in Ae. aegypti to date within a city. We provide evidence for the direct connection between programmatic citywide pyrethroid spraying and the increase in frequency of specific kdr haplotypes by identifying two evolutionary events in the population. The relatively high selection coefficients, even under infrequent insecticide pressure, emphasizes how quickly populations can evolve. The observed rapid increase in frequency of resistance alleles might have been aided by the incomplete dominance of resistance-conferring alleles over corresponding susceptibility alleles. In addition to dramatic temporal shifts, spatial suppression experiments reveal that genetic heterogeneity existed not only at the citywide scale, but also on a very fine scale within the city.

evolutionary biology↗

bioRxiv2021

A large-scale transcriptome-wide association study (TWAS) of ten blood cell phenotypes reveals complexities of TWAS fine-mapping

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases. Hematological measures are highly heritable, and although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) using PrediXcan to systematically investigate the association between genetically-predicted gene expression and hematological measures in 54,542 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We found 239 significant gene-trait associations with hematological measures. Among this set of 239 associations, we replicated 71 at p < 0.05 with same direction of effect for the blood cell trait in a meta-analysis of TWAS results consisting of up to 35,900 European ancestry individuals from the Womens Health Initiative (WHI), the Atherosclerosis Risk in Communities Study (ARIC), and BioMe Biobank. We further attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with these hematological measures, and performed further fine-mapping of TWAS loci. To assist with the interpretation of TWAS findings, we designed an R Shiny application to interactively visualize TWAS results, one genomic locus at a time, by integrating our TWAS results with additional genetic data sources (GWAS, TWAS from other gene expression reference panels, conditional analyses, known GWAS variants, etc.). Our results and R Shiny application highlight frequently overlooked challenges with TWAS and illustrate the complexity of TWAS fine-mapping efforts. Author SummaryTranscriptome-wide association studies (TWAS) have shown great promise in furthering our understanding of the genetic regulatory mechanisms underlying complex trait variation. However, interpreting TWAS results can be incredibly complex, especially in large-scale analyses where hundreds of signals appear throughout the genome, with multiple genes often identified in a single chromosomal region. Our research demonstrates this complexity through real data examples from our analysis of hematological traits, and we provide a useful web application to visualize TWAS results in a broadly approachable format. Together, our results and web application illustrate the importance of interpreting TWAS studies in context and highlight the need to carefully examine results in a region-wide context to draw reasonable conclusions and formulate mechanistic hypotheses.

genetics↗

bioRxiv2020

Lifestyle Risk Score for aggregating multiple lifestyle factors: Handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly liberal. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p-values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach to handle heterogeneity in the LRS based genome-wide interaction meta-analyses.

genetics↗

bioRxiv2020

Efficient gene-environment interaction tests for large biobank-scale sequencing studies

Complex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

genetics↗

bioRxiv2019

Impact of rare and common genetic variants on diabetes diagnosis by hemoglobin A1c in multi-ancestry cohorts: The Trans-Omics for Precision Medicine Program.

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in patients with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K/FN3KRP in Europeans and G6PD in African-Americans and Hispanics) and discovered a new African-ancestry specific low-frequency variant (rs1039215 in HBG2/HBE1, minor allele frequency (MAF)=0.03). The most associated G6PD variant (p.Val98Met, rs1050828-T, MAF=12% in African-Americans, MAF=2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693 - p.Leu353Pro, MAF=0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African-American cohorts and replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

genetics↗

bioRxiv2019

Role of Rare and Low Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

BackgroundAlcohol intake influences plasma lipid levels and such effects may be modulated by genetic variants. ObjectiveWe aimed to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. DesignIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 34,153 European Americans from five discovery studies and 32,275 individuals from six replication studies. Rare and low-frequency protein coding variants (minor allele frequency [≤] 5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction (GxE) test and a joint test of genetic main and GxE interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least two drinks per week, were considered. ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3 and CD300LG) remained significant after conditioning on the common index single nucleotide polymorphism (SNP) identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on TG was discovered at a Bonferroni corrected significance level (p-value <5x10-5) and replicated (p-value <0.013 for the interaction test) in SMC5. ConclusionsIn conclusion, this study applied new gene-based statistical approaches to uncover the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels.

epidemiology↗

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

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole Genome Sequencing Studies

Whole genome sequencing (WGS) studies are being widely conducted to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set based analyses are commonly used to analyze rare variants. However, existing variant-set based approaches need to pre-specify genetic regions for analysis, and hence are not directly applicable to WGS data due to the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding window method requires pre-specifying fixed window sizes, which are often unknown as a priori, are difficult to specify in practice and are subject to limitations given genetic association region sizes are likely to vary across the genome and phenotypes. We propose a computationally-efficient and dynamic scan statistic method (Scan the Genome (SCANG)) for analyzing WGS data that flexibly detects the sizes and the locations of rare-variants association regions without the need of specifying a prior fixed window size. The proposed method controls the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected rare variants association region sizes to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative rare-variant association detection methods while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

genetics↗

bioRxiv2018

ACAT: A Fast and Powerful P-value Combination Method for Rare-variant Analysis in Sequencing Studies

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes and effect directions of the causal variants and the choices of weights. Here we propose an Aggregated Cauchy Association Test (ACAT), a general, powerful and computationally efficient p-value combination method to boost power in sequencing studies. First, by combining variant-level p-values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of casual variants in a variant set. Second, by combining different variant set-level p-values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests including the burden test, Sequence Kernel Association Test (SKAT) and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and burden test, and that ACAT-O has a substantially more robust and higher power than the alternative tests.

genetics↗

bioRxiv2018

Model-based assessment of public health impact and cost-effectiveness of dengue vaccination following screening for prior exposure

The tetravalent dengue vaccine CYD-TDV (Dengvaxia(R)) is the first licensed vaccine against dengue, but recent findings indicate an elevated risk of severe disease among vaccinees without prior dengue virus (DENV) exposure. The World Health Organization currently recommends CYD-TDV only for individuals with serological confirmation of past DENV exposure. Our objective was to evaluate the potential impact and cost-effectiveness of vaccination following serological screening. To do so, we used an agent-based model to simulate DENV transmission with and without vaccination over a 30-year timeframe. Under a range of values for the proportion of vaccinees with prior DENV exposure, we projected the proportion of symptomatic and hospitalized cases averted as a function of the sensitivity and specificity of serological screening. Scenarios about the cost-effectiveness of screening and vaccination were chosen to be representative of Brazil and the Philippines. We found that public health impact depended primarily on the sensitivity of serological screening in high-transmission settings and on a combination of sensitivity and specificity in low-transmission settings. Reducing risk from an individual perspective required high specificity, no less than 0.8. Cost-effectiveness could be achievable from the perspective of a public payer provided that screening sensitivity for prior DENV exposure and the value of a disability-adjusted life-year were both high. Cost-effectiveness was also achievable from an individual perspective, particularly with high screening specificity and low coverage. Whereas the results of this analysis offer general guidelines about CYD-TDV vaccination, decisions in specific contexts would benefit from additional, more context-specific modeling analyses. In conclusion, vaccination with CYD-TDV following serological screening could have a positive impact in certain epidemiological settings, provided that screening is highly specific, at least moderately sensitive, and inexpensive.

epidemiology↗

bioRxiv2017

Model-based analysis of experimental hut data elucidates multifaceted effects of a volatile chemical on Aedes aegypti mosquitoes

BackgroundInsecticides used against Aedes aegypti and other disease vectors can elicit a multitude of dose-dependent effects on behavioral and bionomic traits. Estimating the potential epidemiological impact of a product requires thorough understanding of these effects and their interplay at different dosages. Volatile spatial repellent (SR) products come with an additional layer of complexity due to the potential for movement of affected mosquitoes or volatile particles of the product beyond the treated house. Here, we propose a statistical inference framework for estimating these nuanced effects of volatile SRs.\n\nMethodsWe fitted a continuous-time Markov chain model in a Bayesian framework to mark-release-recapture (MRR) data from an experimental hut study conducted in Iquitos, Peru. We estimated the effects of two dosages of transfluthrin on Ae. aegypti behaviors associated with human-vector contact: repellency, exiting, and knockdown in the treated space and in \"downstream\" adjacent huts. We validated the framework using simulated data.\n\nResultsThe odds of a female Ae. aegypti being repelled from a treated hut (HT) increased at both dosages (low dosage: odds = 1.64, 95% highest density interval (HDI) = 1.30-2.09; high dosage: odds = 1.35, HDI = 1.04-1.67). The relative risk of exiting from the treated hut was reduced (low: RR = 0.70, HDI = 0.62-1.09; high: RR = 0.70, HDI = 0.40-1.06), with this effect carrying over to untreated spaces as far as two huts away from the treated hut (H2) (low: RR = 0.79, HDI = 0.59-1.01; high: RR = 0.66, HDI = 0.50-0.87). Knockdown rates were increased in both treated and downstream huts, particularly under high dosage (HT: RR = 8.37, HDI = 2.11-17.35; H1: RR = 1.39, HDI = 0.52-2.69; H2: RR = 2.22, HDI = 0.96-3.86).\n\nConclusionsOur statistical inference framework is effective at elucidating multiple effects of volatile chemicals used in SR products, as well as their downstream effects. This framework provides a powerful tool for early selection of candidate SR product formulations worth advancing to costlier epidemiological trials, which are ultimately necessary for proof of concept of public health value and subsequent formal endorsement by health authorities.

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