Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State

The Morbidity and Mortality Weekly Reports of the U.S. Centers for Disease Control and Prevention document a raw proxy for counts of pertussis cases in the U.S., and the Project Tycho (PT) database provides an improved source of these weekly data. These data are limited because of reporting delays, variation in state-level surveillance practices, and changes over time in diagnosis methods. We aim to assess whether Google Trends (GT) search data track pertussis incidence relative to PT data and if sociodemographic characteristics explain some variation in the accuracy of state-level models. GT and PT data were used to construct auto-correlation corrected linear models for pertussis incidence in 2004&ndash;2011 for the entire U.S. and each individual state. The national model resulted in a moderate correlation (adjusted R2&thinsp;=&thinsp;0.2369, p&thinsp;&lt;&thinsp;0.05), and state models tracked PT data for some but not all states. Sociodemographic variables explained approximately 30% of the variation in performance of individual state-level models. The significant correlation between GT models and public health data suggests that GT is a potentially useful pertussis surveillance tool. However, the variable accuracy of this tool by state suggests GT surveillance cannot be applied in a uniform manner across geographic sub-regions.</p

Ancestral Diversity in Lipoprotein(a) Studies Helps Address Evidence Gaps

INTRODUCTION: The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in \u3e1.5 billion individuals worldwide, but studies have prioritised European populations. METHODS: Here, we examined how ancestrally diverse studies could clarify Lp(a)\u27s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations. RESULTS: Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R CONCLUSIONS: Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps

Poly-omic risk scores predict inflammatory bowel disease diagnosis

ABSTRACTInflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures

Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits.

It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets

Polygenic prediction of atopic dermatitis improves with atopic training and filaggrin factors

BackgroundWhile numerous genetic loci associated with atopic dermatitis (AD) have been discovered, to date, work leveraging the combined burden of AD risk variants across the genome to predict disease risk has been limited.ObjectivesThis study aims to determine whether polygenic risk scores (PRSs) relying on genetic determinants for AD provide useful predictions for disease occurrence and severity. It also explicitly tests the value of including genome-wide association studies of related allergic phenotypes and known FLG loss-of-function (LOF) variants.MethodsAD PRSs were constructed for 1619 European American individuals from the Atopic Dermatitis Research Network using an AD training dataset and an atopic training dataset including AD, childhood onset asthma, and general allergy. Additionally, whole genome sequencing data were used to explore genetic scoring specific to FLG LOF mutations.ResultsGenetic scores derived from the AD-only genome-wide association studies were predictive of AD cases (PRSAD: odds ratio [OR], 1.70; 95% CI, 1.49-1.93). Accuracy was first improved when PRSs were built off the larger atopy genome-wide association studies (PRSAD+: OR, 2.16; 95% CI, 1.89-2.47) and further improved when including FLG LOF mutations (PRSAD++: OR, 3.23; 95% CI, 2.57-4.07). Importantly, while all 3 PRSs correlated with AD severity, the best prediction was from PRSAD++, which distinguished individuals with severe AD from control subjects with OR of 3.86 (95% CI, 2.77-5.36).ConclusionsThis study demonstrates how PRSs for AD that include genetic determinants across atopic phenotypes and FLG LOF variants may be a promising tool for identifying individuals at high risk for developing disease and specifically severe disease

Ancestral diversity in lipoprotein(a) studies helps address evidence gaps

Introduction The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in &gt;1.5 billion individuals worldwide, but studies have prioritised European populations.Methods Here, we examined how ancestrally diverse studies could clarify Lp(a)’s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.Results Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a ‘rule out’ for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%–99.9%) across PRS thresholds (80th–99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10−6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.Conclusions Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps

Reconsidering evidence for the suppression model of the octave illusion

The octave illusion is elicited by a sequence of tones presented to each ear that continuously alternate in frequency by one octave, but with high and low frequencies always in different ears. The percept for most listeners is a high pitch in one ear, alternating with a low pitch in the other ear. The influential suppression model of the illusion proposed by Deutsch and Roll (1976) carries three postulates: first, that listeners perceive only the pitch of the tones presented to their dominant ear; second, that this pitch is heard in whichever ear received the higher frequency tone; and third, that this apparent dissociation between what and where mechanisms arises from sequential interactions between the tones. In the present article, we reappraise evidence for the suppression model and demonstrate (1) the incompatibility of the theory with the existing literature on pitch perception, sound localization, and ear dominance and (2) methodological limitations in studies that have claimed to provide support for the suppression model. We conclude by proposing an alternative theory of the octave illusion that is based on established principles of fusion, rather than suppression, between ears