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The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting
The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that are thought to underlie, or are closely associated with human inherited disease. At the time of writing (June 2020), the database contains in excess of 289,000 different gene lesions identified in over 11,100 genes manually curated from 72,987 articles published in over 3100 peer-reviewed journals. There are primarily two main groups of users who utilise HGMD on a regular basis; research scientists and clinical diagnosticians. This review aims to highlight how to make the most out of HGMD data in each setting.
Journal Article
Big shot
\"I can't believe I'm letting my ex-boss talk me into working for him again. He's arrogant. Domineering. He gets under my skin in ways I don't want to admit. When I quit, it felt great. But now, seeing the big, bad billionaire rendered helpless by a baby, I give in to his demand. And I'm worried it won't be the last time\"--Back cover.
Book
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
The Human Gene Mutation Database (HGMD
®
) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD (
http://www.hgmd.org
) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.
Journal Article
Congenital idiopathic megaesophagus in the German shepherd dog is a sex-differentiated trait and is associated with an intronic variable number tandem repeat in Melanin-Concentrating Hormone Receptor 2
Congenital idiopathic megaesophagus (CIM) is a gastrointestinal (GI) motility disorder of dogs in which reduced peristaltic activity and dilation of the esophagus prevent the normal transport of food into the stomach. Affected puppies regurgitate meals and water, fail to thrive, and experience complications such as aspiration pneumonia that may necessitate euthanasia. The German shepherd dog (GSD) has the highest disease incidence, indicative of a genetic predisposition. Here, we discover that male GSDs are twice as likely to be affected as females and show that the sex bias is independent of body size. We propose that female endogenous factors ( e . g ., estrogen) are protective via their role in promoting relaxation of the sphincter between the esophagus and stomach, facilitating food passage. A genome-wide association study for CIM revealed an association on canine chromosome 12 ( P -val = 3.12x10 -13 ), with the lead SNPs located upstream or within Melanin-Concentrating Hormone Receptor 2 ( MCHR2 ), a compelling positional candidate gene having a role in appetite, weight, and GI motility. Within the first intron of MCHR2 , we identified a 33 bp variable number tandem repeat (VNTR) containing a consensus binding sequence for the T-box family of transcription factors. Across dogs and wolves, the major allele includes two copies of the repeat, whereas the predominant alleles in GSDs have one or three copies. The single-copy allele is strongly associated with CIM ( P -val = 1.32x10 -17 ), with homozygosity for this allele posing the most significant risk. Our findings suggest that the number of T-box protein binding motifs may correlate with MCHR2 expression and that an imbalance of melanin-concentrating hormone plays a role in CIM. We describe herein the first genetic factors identified in CIM: sex and a major locus on chromosome 12, which together predict disease state in the GSD with greater than 75% accuracy.
Journal Article
Performance Comparison of Genomic Best Linear Unbiased Prediction and Four Machine Learning Models for Estimating Genomic Breeding Values in Working Dogs
This study investigates the efficacy of various genomic prediction models—Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—in predicting genomic breeding values (gEBVs). The phenotypic data include three binary health traits (anodontia, distichiasis, oral papillomatosis) and one behavioral trait (distraction) in a population of guide dogs. These traits impact the potential for success in guide dogs and are therefore routinely characterized but were chosen based on differences in heritability and case counts specifically to assess gEBV model performance. Utilizing a dataset from The Seeing Eye organization, which includes German Shepherds (n = 482), Golden Retrievers (n = 239), Labrador Retrievers (n = 1188), and Labrador and Golden Retriever crosses (n = 111), we assessed model performance within and across different breeds, trait heritability, case counts, and SNP marker densities. Our results indicate that no significant differences were found in model performance across varying heritabilities, case counts, or SNP densities, with all models performing similarly. Given its lack of need for parameter optimization, GBLUP was the most efficient model. Distichiasis showed the highest overall predictive performance, likely due to its higher heritability, while anodontia and distraction exhibited moderate accuracy, and oral papillomatosis had the lowest accuracy, correlating with its low heritability. These findings underscore that lower density SNP datasets can effectively construct gEBVs, suggesting that high-cost, high-density genotyping may not always be necessary. Additionally, the similar performance of all models indicates that simpler models like GBLUP, which requires less fine tuning, may be sufficient for genomic prediction in canine breeding programs. The research highlights the importance of standardized phenotypic assessments and carefully constructed reference populations to optimize the utility of genomic selection in canine breeding programs.
Journal Article
Breeding values and index creation for health and behavior traits in Labrador Retriever guide dogs
Genomic breeding values and multi-trait selection indices have significantly advanced genetic improvement in livestock but remain underutilized in guide dog breeding. This study developed a genomically informed selection framework for a population of Labrador Retrievers by integrating health (e.g., dental, ocular, and dermatological conditions) and behavioral (e.g., trainability, distraction level, pace) traits into a \"Behavior Score,\" \"Health Score,\" and \"Total Score\" index by applying Genomic Best Linear Unbiased Prediction (GBLUP) to estimate breeding values.
Phenotypic and genotypic data were collected from 844 dogs over 26 years at The Seeing Eye guide dog school. Predictive performance was evaluated via five-fold cross-validation and correlation-based metrics. Results showed that some dentition related health traits exhibited moderate to high Area Under Receiving Operating Characteristic (AUROC) values (0.79-0.87), indicating potential for immediate use for genetic improvement. In contrast, most other health traits demonstrated weak to moderate predictive accuracy. Behavioral traits exhibited lower predictive accuracy but showed a stronger association with training success. Models were commonly unable to correctly classify individuals for binary or ordinal traits yet performed well in ranking individuals, likely due to lower heritability or strong environmental influences of traits or limitations of the dataset itself. The behavior-focused Total Score (AUROC ~0.72) outperformed health-based indices as a fixed effect in predicting breeding success despite the weaker predictive ability of individual behavioral traits. Incorporating parental scores as fixed effects modestly improved breeding values for success, indicating the importance of integrating additional data sources where available.
While these findings underscore the utility of genomic selection for guide dog breeding, they also highlight constraints stemming from small, genetically homogeneous populations and variable phenotyping. Ultimately, we provide the first usable individual and multi-trait genomic approaches to enhance both health and performance outcomes in working dog programs and a foundation to expand upon the reference population and behavioral trait assessment to improve prediction accuracy in the future.
Journal Article
A test of the viability of fluid–wall rock interaction mechanisms for changes in opaque phase assemblage in metasedimentary rocks in the Kambalda-St. Ives goldfield, Western Australia
Transitions from pyrrhotite–magnetite- to pyrite–magnetite- and pyrite–hematite-bearing assemblages in metasedimentary rocks in the Kambalda-St. Ives goldfield have been shown to be spatially associated with economic gold grades. Fluid mixing, fluid–rock interaction and phase separation have been proposed previously as causes for this association. Textural, mineralogical and isotopic evidence is reviewed, and thermodynamic calculations are used to investigate the mineralogical consequences of progressive fluid–rock interaction in interflow metasediments. Fluid–rock interactions in response to fluid infiltration and/or bulk composition variation are plausible mechanisms for production of the observed features.
Journal Article
A Predator Unmasked: Life Cycle of Bdellovibrio bacteriovorus from a Genomic Perspective
Predatory bacteria remain molecularly enigmatic, despite their presence in many microbial communities. Here we report the complete genome of Bdellovibrio bacteriovorus HD100, a predatory Gram-negative bacterium that invades and consumes other Gram-negative bacteria. Its surprisingly large genome shows no evidence of recent gene transfer from its prey. A plethora of paralogous gene families coding for enzymes, such as hydrolases and transporters, are used throughout the life cycle of B. bacteriovorus for prey entry, prey killing, and the uptake of complex molecules.
Journal Article