Background

Fenofibrate (Fb) is a known treatment for elevated triglyceride (TG) levels. The Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was designed to investigate potential contributors to the effects of Fb on TG levels. Here, we summarize the analyses of 8 papers whose authors had access to the GOLDN data and were grouped together because they pursued investigations into Fb treatment responses as part of GAW20. These papers report explorations of a variety of genetics, epigenetics, and study design questions. Data regarding treatment with 160 mg of micronized Fb per day for 3 weeks included pretreatment and posttreatment TG and methylation levels (ML) at approximately 450,000 epigenetic markers (cytosine-phosphate-guanine [CpG] sites). In addition, approximately 1 million single-nucleotide polymorphisms (SNPs) were genotyped or imputed in each of the study participants, drawn from 188 pedigrees.

Results

The analyses of a variety of subsets of the GOLDN data used a number of analytic approaches such as linear mixed models, a kernel score test, penalized regression, and artificial neural networks.

Conclusions

Results indicate that (a) CpG ML are responsive to Fb; (b) CpG ML should be included in models predicting the TG level responses to Fb;

White Matter Disease is increasingly being recognized as an important cause of cognitive decline and dementia. Various investigations have linked chronic diet-related conditions to the development of white matter lesions, which appear as white matter hyperintensities on T2-weighted magnetic resonance imaging (MRI) scans of the brain. Thus, it can be postulated that the metabolic, inflammatory, and microvascular changes accompanying a western diet, hyperlipidemia, hypertension, and diabetes mellitus type II (DMII) are potential mediators in the development and progression of white matter disease, which in turn contributes to the development and progression of cognitive decline. This review will examine evidence for potential metabolic, inflammatory, and microvascular determinants of white matter disease and cognitive decline. Specifically, we will focus on the effects of altered insulin signaling in diabetes, obesity-induced oxidative stress, neuroinflammation, arterial stiffness due to hypertension, ischemia secondary to cerebral small vessel disease, and blood brain barrier disturbances.

Background

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

Results

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

Conclusions

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.