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Background Variant interpretation is the main bottleneck in medical genomic sequencing efforts. This usually involves genome analysts manually searching through a multitude of independent databases, often with the aid of several, mostly independent, computational tools. To streamline variant interpretation, we developed the GeneTerpret platform which collates data from current interpretation tools and databases, and applies a phenotype-driven query to categorize the variants identified in the genome(s). The platform assigns quantitative validity scores to genes by query and assembly of the genotype–phenotype data, sequence homology, molecular interactions, expression data, and animal models. It also uses the American College of Medical Genetics and Genomics (ACMG) criteria to categorize variants into five tiers of pathogenicity. The final output is a prioritized list of potentially causal variants/genes. Results We tested GeneTerpret by comparing its performance to expert-curated genes (ClinGen’s gene-validity database) and variant pathogenicity reports (DECIPHER database). Output from GeneTerpret was 97.2% and 83.5% concordant with the expert-curated sources, respectively. Additionally, similar concordance was observed when GeneTerpret’s performance was compared with our internal expert-interpreted clinical datasets. Conclusions GeneTerpret is a flexible platform designed to streamline the genome interpretation process, through a unique interface, with improved ease, speed and accuracy. This modular and customizable system allows the user to tailor the component-programs in the analysis process to their preference. GeneTerpret is available online at https://geneterpret.com .

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page—supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP , a video tutorial series is available at https://bit.ly/SCIPVideos ).

Over the past two decades, an increasing amount of phylogeographic work has substantially improved our understanding of African biogeography, in particular the role played by Pleistocene pluvial-drought cycles on terrestrial vertebrates. However, still little is known on the evolutionary history of semi-aquatic animals, which faced tremendous challenges imposed by unpredictable availability of water resources. In this study, we investigate the Late Pleistocene history of the common hippopotamus (Hippopotamus amphibius), using mitochondrial and nuclear DNA sequence variation and range-wide sampling. We documented a global demographic and spatial expansion approximately 0.1-0.3 My ago, most likely associated with an episode of massive drainage overflow. These events presumably enabled a historical continent-wide gene flow among hippopotamus populations, and hence no clear continental-scale genetic structuring remains. Nevertheless, present-day hippopotamus populations are genetically disconnected, probably as a result of the mid-Holocene aridification and contemporary anthropogenic pressures. This unique pattern contrasts with the biogeographic paradigms established for savannah-adapted ungulate mammals and should be further investigated in other water-associated taxa. Our study has important consequences for the conservation of the hippo, an emblematic but threatened species that requires specific protection to curtail its long-term decline. This article is protected by copyright. All rights reserved.

Udgivelsesdato: null-null, We investigated population genetic structure and regional differentiation among African savannah elephants in Kenya using mitochondrial and microsatellite markers. We observed mitochondrial DNA (mtDNA) nucleotide diversity of 1.68% and microsatellite variation in terms of average number of alleles, expected and observed heterozygosities in the total study population of 10.20, 0.75, and 0.69, respectively. Hierarchical analysis of molecular variance of mtDNA variation revealed significant differentiation among the 3 geographical regions studied (F(CT) = 0.264; P < 0.05) and a relatively lower differentiation among populations within regions (F(SC) = 0.218; P < 0.0001). Microsatellite variation significantly differentiated among populations within regions (F(SC) = 0.019; P < 0.0001) but not at the regional levels (F(CT) = 0.000; P > 0.500). We attribute the high differentiation at the mitochondrial genome to the matrilineal social structure of elephant populations, female natal philopatry, and probably ancient vicariance. Lack of significant regional differentiation at the nuclear loci vis-a-vis strong differences at mtDNA loci between regions is likely the effect of subsequent homogenization through male-mediated gene flow. Our results depicting 3 broad regional mtDNA groups and the observed population genetic differentiation as well as connectivity patterns should be incorporated in the planning of future management activities such as translocations.