[PLoS Genetics]
Optimal sequencing strategies for identifying disease-associated singletons
“We show that the power to detect singletons increases with coverage, typically plateauing for coverage > ~25x. Next, we show that, when total sequencing capacity is fixed, the power of association studies focused on singletons is typically maximized for coverage of 15-20x, independent of relative risk, disease prevalence, singleton burden, and case-control ratio. Our results suggest sequencing depth of 15-20x as an appropriate compromise of singleton detection power and sample size for studies of rare variants in complex disease.” Work from Abecasis.
[PMID:28617416] [Genetics in Medicine]
Genome-wide cfDNA screening: clinical laboratory experience with the first 10,000 cases
“the first description of clinical experience with genome-wide cfDNA analysis for prenatal screening, within the first year of the test’s introduction”
[PMID:28604721] [Nature Methods]
Comparison of computational methods for Hi-C data analysis
“We quantitatively compared the performance of 13 algorithms in their analyses of Hi-C data from six landmark studies and simulations. This comparison revealed differences in the performance of methods for chromatin interaction identification, but more comparable results for topologically associating domains (TAD) detection between algorithms.”
Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing
“deep single-cell RNA sequencing on 5,063 single T cells isolated from peripheral blood, tumor, and adjacent normal tissues from six hepatocellular carcinoma patients.”
 [Genome Biology]
Increasing mapping precision of genome-wide association studies: to genotype and impute, sequence, or both?
The war between CDCV and CDRV continues. How far the causal variant(s) away from the GWAS hits? Still many different answers.
An Expanded View of Complex Traits: From Polygenic to Omnigenic
“A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome—including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an “omnigenic” model.”
[PMID:28594829] [PLOS Computational Biology]
Mendel,MD: A user-friendly open-source web tool for analyzing WES and WGS in the diagnosis of patients with Mendelian disorders
“combines multiple types of filter options and makes use of regularly updated databases to facilitate exome and genome annotation, the filtering process and the selection of candidate genes and variants for experimental validation and possible diagnosis. This tool offers a user-friendly interface, and leads clinicians through simple steps by limiting the number of candidates to achieve a final diagnosis of a medical genetics case. A useful innovation is the “1-click” method, which enables listing all the relevant variants in genes present at OMIM for perusal by clinicians.”