Evaluating the pathogenicity of a variant is challenging given the plethora of types of genetic evidence that laboratories consider. Deciding how to weigh each type of evidence is difficult, and standards have been needed. In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for the assessment of variants in genes associated with Mendelian diseases. Nine molecular diagnostic laboratories involved in the Clinical Sequencing Exploratory Research (CSER) consortium piloted these guidelines on 99 variants spanning all categories (pathogenic, likely pathogenic, uncertain significance, likely benign, and benign). Nine variants were distributed to all laboratories, and the remaining 90 were evaluated by three laboratories. The laboratories classified each variant by using both the laboratory's own method and the ACMG-AMP criteria. The agreement between the two methods used within laboratories was high (K-alpha = 0.91) with 79% concordance. However, there was only 34% concordance for either classification system across laboratories. After consensus discussions and detailed review of the ACMG-AMP criteria, concordance increased to 71%. Causes of initial discordance in ACMG-AMP classifications were identified, and recommendations on clarification and increased specification of the ACMG-AMP criteria were made. In summary, although an initial pilot of the ACMG-AMP guidelines did not lead to increased concordance in variant interpretation, comparing variant interpretations to identify differences and having a common framework to facilitate resolution of those differences were beneficial for improving agreement, allowing iterative movement toward increased reporting consistency for variants in genes associated with monogenic disease.

On page 1072 in the originally published version of this article, PS2 was a typo and should have read PS3 in the following sentence: ''The other most common examples of modified strength included the following: PVS1 (a predicted null variant in a gene where LOF is a known mechanism of disease) was downgraded from very strong four times, PS2 (well-established functional studies show a deleterious effect) was downgraded three times, and BS1 (MAF is too high for the disorder) was downgraded three times.'' The error has been corrected online, and the authors apologize for the oversight.

Summary Cancer progression depends on both cell-intrinsic processes and interactions between different cell types. However, large scale assessment of cell type composition and molecular profiles of individual cell types within tumors remains challenging. To address this, we developed Epigenomic Deconvolution (EDec), an in silico method that infers cell type composition of complex tissues as well as DNA methylation and gene transcription profiles of constituent cell types. By applying EDec to The Cancer Genome Atlas (TCGA) breast tumors we detect changes in immune cell infiltration related to patient prognosis, and a striking change in stromal fibroblast to adipocyte ratio across breast cancer subtypes. We further show that a less adipose stroma tends to display lower levels of mitochondrial activity and to be associated with cancerous cells with higher levels of oxidative metabolism. These findings highlight the role of stromal composition in the metabolic coupling between distinct cell types within tumors.

To assess the impact of genetic variation in regulatory loci on human health, we construct a high-resolution map of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep whole-genome bisulfite sequencing of 49 methylomes reveals sequence-dependent CpG methylation imbalances at thousands of heterozygous regulatory loci. Such loci are enriched for stochastic switching, defined as random transitions between fully methylated and unmethylated states of DNA. The methylation imbalances at thousands of loci are explainable by different relative frequencies of the methylated and unmethylated states for the two alleles. Further analyses provide a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, and disease-associated

The structure and dynamics of a single GM1 (Gal5-beta1,3-GalNAc4-beta1,4-(NeuAc3-alpha2,3)-Gal2-beta1,4-Glc1-beta1,1-Cer) embedded in a DPPC bilayer have been studied by MD simulations. Eleven simulations, each of 10 ns productive run, were performed with different initial conformations of GM1. Simulations of GM1-Os in water and of a DPPC bilayer were also performed to delineate the effects of the bilayer and GM1 on the conformational and orientational dynamics of each other. The conformation of the GM1 headgroup observed in the simulations is in agreement with those reported in literature; but the headgroup is restricted when embedded in the bilayer. NeuAc3 is the outermost saccharide towards the water phase. Glc1 and Gal2 prefer a parallel, and NeuAc3, GalNac4 and Gal5 prefer a perpendicular, orientation with respect to the bilayer normal. The overall characteristics of the bilayer are not affected by the presence of GM1; however, GM1 does influence the DPPC molecules in its immediate vicinity. The implications of these observations on the specific recognition and binding of GM1 embedded in a lipid bilayer by exogenous proteins as well as proteins embedded in lipids have been discussed.