The 1000 Genomes Project: deep genomic sequencing waiting for deep psychiatric phenotyping

Hypomethylating agents (HMAs) show benefit in AML rather than in intermediate/high-risk MDS based on genetic mutations in epigenetic modification (EMMs): from a retrospective study

Since HMAs were recommended for treatments in AML and MDS, we wondered whether HMAs could provide similar benefit to AML and intermediate/high-risk MDS under the direction of next-generation sequencing. Here we retrospectively analyzed the prognosis of 176 AML and 128 intermediate/high-risk MDS patients treated with HMAs or non-HMA regimens. For AML, HMAs regimen was related to better CR rate compared with non-HMA regimen in elder cohort, while the situation was the opposite in younger cohort. In consolidation phase, EMM (+) patients could benefit from HMAs regimen. Relapsed AML patients receiving HMAs regimen rather than non-HMA regimen had better post-relapse survival. Multivariate analysis identified HMA regimen as an independent prognostic factor for OS in EMM (+) cohort. For intermediate/high-risk MDS patients not undergoing HSCT, however, HMA regimen showed no survival advantage in EMM (+) cohort and was conversely associated with shorter survival in EMM (-) cohort compared with non-HMA regimen. And among those undergoing HSCT, HMA prior to HSCT predicted poor prognosis compared with upfront HSCT regardless of the existence of EMMs. Therefore, HMAs had better therapeutic value in AML rather than in intermediate/high-risk MDS based on EMMs.

Bayesian systems-based genetic association analysis with effect strength estimation and omic wide interpretation: a case study in rheumatoid arthritis

Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex intermediate clinical phenotypes are typically used in follow-up candidate gene association studies, e.g., after genome-wide analysis, to deepen the understanding of the associations and to estimate effect strength. This chapter discusses a solid methodology, which is useful in such a scenario, by using probabilistic graphical models, namely, Bayesian networks in the Bayesian statistical framework. This method offers systematically scalable, comprehensive hierarchical hypotheses about multivariate relevance. We discuss its workflow: from data engineering to semantic publication of the results. We overview the construction, visualization, and interpretation of complex hypotheses related to the structural analysis of relevance. Furthermore, we illustrate the use of a dependency model-based relevance measure, which takes into account the structural properties of the model, for quantifying the effect strength. Finally, we discuss the "interpretational" or translational challenge of a genetic association study, with a focus on the fusion of heterogeneous omic knowledge to reintegrate the results into a genome-wide context.

Early-onset dementias: diagnostic and etiological considerations

This paper summarizes the body of literature about early-onset dementia (EOD) that led to recommendations from the Fourth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia. A broader differential diagnosis is required for EOD compared with late-onset dementia. Delays in diagnosis are common, and the social impact of EOD requires special care teams. The etiologies underlying EOD syndromes should take into account family history and comorbid diseases, such as cerebrovascular risk factors, that may influence the clinical presentation and age at onset. For example, although many EODs are more likely to have Mendelian genetic and/or metabolic causes, the presence of comorbidities may drive the individual at risk for late-onset dementia to manifest the symptoms at an earlier age, which contributes further to the observed heterogeneity and may confound diagnostic investigation. A personalized medicine approach to diagnosis should therefore be considered depending on the age at onset, clinical presentation, and comorbidities. Genetic counseling and testing as well as specialized biochemical screening are often required, especially in those under the age of 40 and in those with a family history of autosomal dominant or recessive disease. Novel treatments in the drug development pipeline for EOD, such as genetic forms of Alzheimer's disease, should target the specific pathogenic cascade implicated by the mutation or biochemical defect.

Insights on personalized medicine from Ayurveda

The "omics" era of research has provided vital information on the genetic and biochemical diversity of individuals. This has lead to the emergence of "personalized medicine," wherein one aims to design specific drugs for individual patients or subtypes of patients. Indeed, the ongoing patent wars on this matter, suggest that personalized medicine represents a major goal for today's pharmaceutical industries. Although the concept of personalized medicine is new to modern medicine, it is a well-established concept in Ayurveda, the traditional system of Indian medicine that is still being practiced. Therefore, this article discusses topics that are crucial for the advancement of modern personalized medicine. These topics include disease susceptibility, disease subtypes, and Ayurvedic therapeutics. First, we explain how Ayurveda, Traditional Chinese Medicine, and Traditional Korean medicine or Sasang Constitutional medicine; conceptualize disease susceptibility and disease subtypes. Next, we focus on conceptual similarities between molecular medicine and Ayurvedic concepts of disease susceptibility and disease subtypes. For each topic, we explain the relevant experimental evidence reported in the literature. We also propose new hypotheses and suggest experimental approaches for their testing and validation.