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Purpose The 2015 American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG–AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification. Methods We undertook an iterative process of refining the ACMG–AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions. Results Our implementation: (i) separated ambiguous ACMG–AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the “clinical criteria” style of the guidelines with additive, semiquantitative criteria. Conclusion Sherloc builds on the strong framework of 33 rules established by the ACMG–AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

Objective Molecular genetic etiologies in epilepsy have become better understood in recent years, creating important opportunities for precision medicine. Building on these advances, detailed studies of the complexities and outcomes of genetic testing for epilepsy can provide useful insights that inform and refine diagnostic approaches and illuminate the potential for precision medicine in epilepsy. Methods We used a multi‐gene next‐generation sequencing (NGS) panel with simultaneous sequence and exonic copy number variant detection to investigate up to 183 epilepsy‐related genes in 9769 individuals. Clinical variant interpretation was performed using a semi‐quantitative scoring system based on existing professional practice guidelines. Results Molecular genetic testing provided a diagnosis in 14.9%‐24.4% of individuals with epilepsy, depending on the NGS panel used. More than half of these diagnoses were in children younger than 5 years. Notably, the testing had possible precision medicine implications in 33% of individuals who received definitive diagnostic results. Only 30 genes provided 80% of molecular diagnoses. While most clinically significant findings were single‐nucleotide variants, ~15% were other types that are often challenging to detect with traditional methods. In addition to clinically significant variants, there were many others that initially had uncertain significance; reclassification of 1612 such variants with parental testing or other evidence contributed to 18.5% of diagnostic results overall and 6.1% of results with precision medicine implications. Significance Using an NGS gene panel with key high‐yield genes and robust analytic sensitivity as a first‐tier test early in the diagnostic process, especially for children younger than 5 years, can possibly enable precision medicine approaches in a significant number of individuals with epilepsy.

Recombination rates seem to vary extensively along the human genome. Pedigree analysis suggests that rates vary by an order of magnitude when measured at the megabase scale 1 , and at a finer scale, sperm typing studies point to the existence of recombination hotspots 2 . These are short regions (1–2 kb) in which recombination rates are 10–1,000 times higher than the background rate. Less is known about how recombination rates change over time. Here we determined to what degree recombination rates are conserved among closely related species by estimating recombination rates from 14 Mb of linkage disequilibrium data in central chimpanzee and human populations. The results suggest that recombination hotspots are not conserved between the two species and that recombination rates in larger (50 kb) genomic regions are only weakly conserved. Therefore, the recombination landscape has changed markedly between the two species.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-nucleotide polymorphisms (SNPs) have been the focus of much attention in human genetics because they are extremely abundant and well-suited for automated large-scale genotyping. Human SNPs, however, are less informative than other types of genetic markers (such as simple-sequence length polymorphisms or microsatellites) and thus more loci are required for mapping traits. SNPs offer similar advantages for experimental genetic organisms such as the mouse, but they entail no loss of informativeness because bi-allelic markers are fully informative in analysing crosses between inbred strains. Here we report a large-scale analysis of SNPs in the mouse genome. We characterized the rate of nucleotide polymorphism in eight mouse strains and identified a collection of 2,848 SNPs located in 1,755 sequence-tagged sites (STSs) using high-density oligonucleotide arrays. Three-quarters of these SNPs have been mapped on the mouse genome, providing a first-generation SNP map of the mouse. We have also developed a multiplex genotyping procedure by which a genome scan can be performed with only six genotyping reactions per animal.

High-density oligonucleotide arrays are among the most rapidly expanding technologies in biology today. In the {\\sl GeneChip} system, the reconstruction of the target concentration depends upon the differential signal generated from hybridizing the target RNA to two nearly identical templates: a perfect match (PM) and a single mismatch (MM) probe. It has been observed that a large fraction of MM probes repeatably bind targets better than the PMs, against the usual expectation from sequence-specific hybridization; this is difficult to interpret in terms of the underlying physics. We examine this problem via a statistical analysis of a large set of microarray experiments. We classify the probes according to their signal to noise (\\(S/N\\)) ratio, defined as the eccentricity of a (PM, MM) pair's `trajectory' across many experiments. Of those probes having large \\(S/N\\) (\\(>3\\)) only a fraction behave consistently with the commonly assumed hybridization model. Our results imply that the physics of DNA hybridization in microarrays is more complex than expected, and they suggest new ways of constructing estimators for the target RNA concentration.

One of the most popular tools for large scale gene expression studies are high-density oligonucleotide (GeneChip(R)) arrays. These currently have 16-20 small probe cells (``features'') for evaluating the transcript abundance of each gene. In addition, each probe is accompanied by a mismatched probe designed as a control for non-specificity. An algorithm is presented to compute comparative expression levels from the intensities of the individual features, based on a statistical study of their distribution. Interestingly, MM probes need not be included in the analysis. We show that our algorithm improves significantly upon the current standard and leads to a substantially larger number of genes brought above the noise floor for further analysis.