Joint analysis for integrating two related studies of different data types and different study designs using hierarchical modeling approaches

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Joint spatial modelling of disease risk using multiple sources: an application on HIV prevalence from antenatal sentinel and demographic and health surveys in Namibia

Background

In disease mapping field, researchers often encounter data from multiple sources. Such data are fraught with challenges such as lack of a representative sample, often incomplete and most of which may have measurement errors, and may be spatially and temporally misaligned. This paper presents a joint model in the effort to deal with the sampling bias and misalignment.

Methods

A joint (bivariate) spatial model was applied to estimate HIV prevalence using two sources: 2014 National HIV Sentinel survey (NHSS) among pregnant women aged 15–49 years attending antenatal care (ANC) and the 2013 Namibia Demographic and Health Surveys (NDHS).

Results

Findings revealed that health districts and constituencies in the northern part of Namibia were found to be highly associated with HIV infection. Also, the study showed that place of residence, gender, gravida, marital status, number of kids dead, wealth index, education, and condom use were significantly associated with HIV infection in Namibia.

Conclusion

This study had shown determinants of HIV infection in Namibia and had revealed areas at high risk through HIV prevalence mapping. Moreover, a joint modelling approach was used in order to deal with spatially misaligned data. Finally, it was shown that prediction of HIV prevalence using the NDHS data source can be enhanced by jointly modelling other HIV data such as NHSS data. These findings would help Namibia to tailor national intervention strategies for specific regions and groups of population.

The soft computing-based approach to investigate allergic diseases: a systematic review

BACKGROUND

Early recognition of inflammatory markers and their relation to asthma, adverse drug reactions, allergic rhinitis, atopic dermatitis and other allergic diseases is an important goal in allergy. The vast majority of studies in the literature are based on classic statistical methods; however, developments in computational techniques such as soft computing-based approaches hold new promise in this field.

OBJECTIVE

The aim of this manuscript is to systematically review the main soft computing-based techniques such as artificial neural networks, support vector machines, bayesian networks and fuzzy logic to investigate their performances in the field of allergic diseases.

METHODS

The review was conducted following PRISMA guidelines and the protocol was registered within PROSPERO database (CRD42016038894). The research was performed on PubMed and ScienceDirect, covering the period starting from September 1, 1990 through April 19, 2016.

RESULTS

The review included 27 studies related to allergic diseases and soft computing performances. We observed promising results with an overall accuracy of 86.5%, mainly focused on asthmatic disease. The review reveals that soft computing-based approaches are suitable for big data analysis and can be very powerful, especially when dealing with uncertainty and poorly characterized parameters. Furthermore, they can provide valuable support in case of lack of data and entangled cause-effect relationships, which make it difficult to assess the evolution of disease.

CONCLUSIONS

Although most works deal with asthma, we believe the soft computing approach could be a real breakthrough and foster new insights into other allergic diseases as well.

Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the PR EDIGT score

Fifty-five years after the concept of dopamine replacement therapy was introduced, Parkinson disease (PD) remains an incurable neurological disorder. To date, no disease-modifying therapeutic has been approved. The inability to predict PD incidence risk in healthy adults is seen as a limitation in drug development, because by the time of clinical diagnosis ≥ 60% of dopamine neurons have been lost. We have designed an incidence prediction model founded on the concept that the pathogenesis of PD is similar to that of many disorders observed in ageing humans, i.e. a complex, multifactorial disease. Our model considers five factors to determine cumulative incidence rates for PD in healthy adults: (i) DNA variants that alter susceptibility (D), e.g. carrying a LRRK2 or GBA risk allele; (ii) Exposure history to select environmental factors including xenobiotics (E); (iii) Gene-environment interactions that initiate pathological tissue responses (I), e.g. a rise in ROS levels, misprocessing of amyloidogenic proteins (foremost, α-synuclein) and dysregulated inflammation; (iv) sex (or gender; G); and importantly, (v) time (T) encompassing ageing-related changes, latency of illness and propagation of disease. We propose that cumulative incidence rates for PD (PR ) can be calculated in healthy adults, using the formula: PR (%) = (E + D + I) × G × T. Here, we demonstrate six case scenarios leading to young-onset parkinsonism (n = 3) and late-onset PD (n = 3). Further development and validation of this prediction model and its scoring system promise to improve subject recruitment in future intervention trials. Such efforts will be aimed at disease prevention through targeted selection of healthy individuals with a higher prediction score for developing PD in the future and at disease modification in subjects that already manifest prodromal signs.