Heidegalbraith7885

Progressive myoclonus epilepsies (PMEs) comprise a group of clinically and genetically heterogeneous rare diseases. Over 70% of PME cases can now be molecularly solved. Known PME genes encode a variety of proteins, many involved in lysosomal and endosomal function. We performed whole-exome sequencing (WES) in 84 (78 unrelated) unsolved PME-affected individuals, with or without additional family members, to discover novel causes. We identified likely disease-causing variants in 24 out of 78 (31%) unrelated individuals, despite previous genetic analyses. The diagnostic yield was significantly higher for individuals studied as trios or families (14/28) versus singletons (10/50) (OR = 3.9, p value = 0.01, Fisher's exact test). The 24 likely solved cases of PME involved 18 genes. First, we found and functionally validated five heterozygous variants in NUS1 and DHDDS and a homozygous variant in ALG10, with no previous disease associations. All three genes are involved in dolichol-dependent protein glycosylation, a pathway not previously implicated in PME. Second, we independently validate SEMA6B as a dominant PME gene in two unrelated individuals. Third, in five families, we identified variants in established PME genes; three with intronic or copy-number changes (CLN6, GBA, NEU1) and two very rare causes (ASAH1, CERS1). Fourth, we found a group of genes usually associated with developmental and epileptic encephalopathies, but here, remarkably, presenting as PME, with or without prior developmental delay. Our systematic analysis of these cases suggests that the small residuum of unsolved cases will most likely be a collection of very rare, genetically heterogeneous etiologies.The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.Genome-wide association studies (GWASs) have enabled unbiased identification of genetic loci contributing to common complex diseases. Because GWAS loci often harbor many variants and genes, it remains a major challenge to move from GWASs' statistical associations to the identification of causal variants and genes that underlie these association signals. Researchers have applied many statistical and functional fine-mapping strategies to prioritize genetic variants and genes as potential candidates. There is no gold standard in fine-mapping approaches, but consistent results across different approaches can improve confidence in the fine-mapping findings. Here, we combined text mining with a systematic review and formed a catalog of 85 studies with evidence of fine mapping for at least one autoimmune GWAS locus. Across all fine-mapping studies, we compiled 230 GWAS loci with allelic heterogeneity estimates and predictions of causal variants and trait-relevant genes. These 230 loci included 455 combinations of locus-by-disease association signals with 15 autoimmune diseases. Using these estimates, we assessed the probability of mediating disease risk associations across genes in GWAS loci and identified robust signals of causal disease biology. We predict that this comprehensive catalog of GWAS fine-mapping efforts in autoimmune disease will greatly help distill the plethora of information in the field and inform therapeutic strategies.Genome sequencing is enabling precision medicine-tailoring treatment to the unique constellation of variants in an individual's genome. The impact of recurrent pathogenic variants is often understood, however there is a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequential variants and associated mechanisms. Variants of uncertain significance (VUSs) in these genes are discovered at a rate that outpaces current ability to classify them with databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings inborn errors of metabolism in newborns and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.When it comes to precision oncology, proteogenomics may provide better prospects to the clinical characterization of tumors, help make a more accurate diagnosis of cancer, and improve treatment for patients with cancer. This perspective describes the significant contributions of The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium to precision oncology and makes the case that proteogenomics needs to be fully integrated into clinical trials and patient care in order for precision oncology to deliver the right cancer treatment to the right patient at the right dose and at the right time.Partial agonism describes the relative efficacy of a drug compared to one that produces a greater response in a particular system; the designation is dependent upon the comparator and the system. In this issue of Cell, Huang et al. describe biophysical approaches to define the signature of GPCR partial agonists, providing direct measures of varying intrinsic efficacy.Co-opting enemy weapons is a proven strategy in warfare. The war of nature is no different. In this issue of Cell, Xia and colleagues show how a major crop pest stole a plant phenolic glucoside malonyltransferase gene, allowing neutralization of a large class of plant defense compounds.Many scientists spend unnecessary time reformatting papers to submit them to different journals. We propose a uniform submission format that we hope journals will include in their options for submission. Widespread adoption of this uniform submission format could shorten the submission and publishing process, freeing up time for research.The macroevolutionary transition from terra firma to obligatory inhabitance of the marine hydrosphere has occurred twice in the history of Mammalia Cetacea and Sirenia. In the case of Cetacea (whales, dolphins, and porpoises), molecular phylogenies provide unambiguous evidence that fully aquatic cetaceans and semiaquatic hippopotamids (hippos) are each other's closest living relatives. Ancestral reconstructions suggest that some adaptations to the aquatic realm evolved in the common ancestor of Cetancodonta (Cetacea + Hippopotamidae). An alternative hypothesis is that these adaptations evolved independently in cetaceans and hippos. Here, we focus on the integumentary system and evaluate these hypotheses by integrating new histological data for cetaceans and hippos, the first genome-scale data for pygmy hippopotamus, and comprehensive genomic screens and molecular evolutionary analyses for protein-coding genes that have been inactivated in hippos and cetaceans. We identified eight skin-related genes that are inactivated in both cetaceans and hippos, including genes that are related to sebaceous glands, hair follicles, and epidermal differentiation. However, none of these genes exhibit inactivating mutations that are shared by cetaceans and hippos. Mean dates for the inactivation of skin genes in these two clades serve as proxies for phenotypic changes and suggest that hair reduction/loss, the loss of sebaceous glands, and changes to the keratinization program occurred ∼16 Ma earlier in cetaceans (∼46.5 Ma) than in hippos (∼30.5 Ma). These results, together with histological differences in the integument and prior analyses of oxygen isotopes from stem hippopotamids ("anthracotheres"), support the hypothesis that aquatic skin adaptations evolved independently in hippos and cetaceans.Animals respond to visual threats, such as a looming object, with innate defensive behaviors. Here, we report that a specific type of retinal ganglion cell (RGC), the OFF-transient alpha RGC, is critical for the detection of looming objects. We identified Kcnip2 as its molecular marker. The activity of the Kcnip2-expressing RGCs encodes the size of the looming object. Ablation or suppression of these RGCs abolished or severely impaired the escape and freezing behaviors of mice in response to a looming object, while activation of their somas in the retina, or their axon terminals in the superior colliculus, triggered immediate escape behavior. Our results link the activity of a single type of RGC to visually triggered innate defensive behaviors and underscore that ethologically significant visual information is encoded by a labeled line strategy as early as in the retina.Over the last two millennia, and at an accelerating pace, the African elephant (Loxodonta spp. Lin.) has been threatened by human activities across its range.1-7 We investigate the correlates of elephant home range sizes across diverse biomes. buy PF-562271 Annual and 16-day elliptical time density home ranges8 were calculated by using GPS tracking data collected from 229 African savannah and forest elephants (L. africana and L. cyclotis, respectively) between 1998 and 2013 at 19 sites representing bushveld, savannah, Sahel, and forest biomes. Our analysis considered the relationship between home range area and sex, species, vegetation productivity, tree cover, surface temperature, rainfall, water, slope, aggregate human influence, and protected area use. Irrespective of these environmental conditions, long-term annual ranges were overwhelmingly affected by human influence and protected area use. Only over shorter, 16-day periods did environmental factors, particularly water availability and vegetation productivity, become important in explaining space use.