Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

The incidence of Alzheimer’s Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer’s Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets.

New Economic and Financial Indicators of Sustainability James Pittman, Kevin WilhelmThe growing risks imposed by climate change and other environmental and social issues ultimately have the potential to erode the very foundations of human civilization. Recent reports on climate change from both the international insurance industry and former Pentagon analysts forecast potential global crises of international security and economic prosperity, but more critically of human health, safety, and survival on earth (Lloyds of London, 2006;Schwartz and Randall, 2003;Swiss Re, 2006). This risk is amplified by use of conventional economic and financial accounting methods by colleges and universities and also by business, government, and other sectors; such methods do not consider the full costs and potential risks of unsustainable performance. We believe it imperative, therefore, to create new and utilize existing appropriate financial indicators that incorporate the principles of sustainability and show the true costs of our actions not only financially but environmentally and socially as well. Continued reliance on flawed financial indicators will create increasingly difficult environmental and social problems.It is commonly noted that what is measured determines what is managed. Economic and financial measures and indicators serve an institution on several fronts: improving institutional management for solid short-term performance as well as responsible institutional transparency to ensure accountability, secure trust, and improve access to needed resources. An opportunity exists to change the financial metrics used to measure the value of sustainability and to demonstrate viable and practical sustainable actions

With the rapid increase in publicly available sequencing data, healthcare professionals are tasked with understanding how genetic variation informs diagnosis and affects patient health outcomes. Understanding the impact of a genetic variant in disease could be used to predict susceptibility/protection and to help build a personalized medicine profile. In the United States, over 3.8 million newborns are screened for several rare genetic diseases each year, and the follow-up testing of screen-positive newborns often involves sequencing and the identification of variants. This presents the opportunity to use longitudinal health information from these newborns to inform the impact of variants identified in the course of diagnosis. To test this, we performed secondary analysis of a 10-year natural history study of individuals diagnosed with metabolic disorders included in newborn screening (NBS). We found 564 genetic variants with accompanying phenotypic data and identified that 161 of the 564 variants (29%) were not included in ClinVar. We were able to classify 139 of the 161 variants (86%) as pathogenic or likely pathogenic. This work demonstrates that secondary analysis of longitudinal data collected as part of NBS finds unreported genetic variants and the accompanying clinical information can inform the relationship between genotype and phenotype.