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Over one thousand blood pressure (BP) loci have been identified by genetic association studies. However, determination of causal genes remains a bottleneck for further translational discovery. Here we triage genes identified by a BP genome-wide association study (GWAS) using optimized machine learning (ML) methodologies. We investigated regression models with nested cross-validation, benchmarking fourteen models (tree-based, ensemble and generalized linear models) using multi-omic features and 293 training genes. The top-performing model was extreme gradient boosting (0.897 predicted r2) that prioritized 794 genes. These genes showed significantly more intolerance to variation and were more often termed as essential. 27/794 genes showed evidence of direct interaction with blood pressure medications potentially highlighting opportunities for genetic stratification of response. Notably some BP drug mechanisms were not well represented in GWAS, while 51 genes showed no interaction with known BP drugs, highlighting possible target and repositioning opportunities. This study exploits ML to prioritize signals within BP-GWAS associations based on similarities with established BP-drug interacting genes, streamlining identification of genes underpinning BP that could inform disease management and drug discovery.

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection.METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis.RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives.CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).

Human phenotype ontology; Prenatal diagnosis; Prenatal phenotyping Ontología del fenotipo humano; Diagnóstico prenatal; Fenotipado prenatal Ontologia del fenotip humà; Diagnòstic prenatal; Fenotipat prenatal Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care. European Commission; National Human Genome Research Institute; NIH Office of the Director; The European Union's EIT-Health Innovation Program bp2020-2022, Grant/Award Numbers: #211015, #20062; NIH Office of the Director (OD), the European Union's Horizon 2020 research and innovation program, Grant/Award Number: 779257; NHGRI, Grant/Award Numbers: 2R24OD011883-05A1, 1U24HG011449-01A1

Altea Lorenzo-Arribas and Pilar Cacheiro delve into Nightingale's trove of correspondence and papers to produce a map of the social reformer's social network

SPTBN1 encodes βII-spectrin, the ubiquitously expressed β-spectrin that forms micrometer-scale networks associated with plasma membranes. Mice deficient in neuronal βII-spectrin have defects in cortical organization, developmental delay and behavioral deficiencies. These phenotypes, while less severe, are observed in haploinsufficient animals, suggesting that individuals carrying heterozygous SPTBN1 variants may also show measurable compromise of neural development and function. Here we identify heterozygous SPTBN1 variants in 29 individuals with developmental, language and motor delays; mild to severe intellectual disability; autistic features; seizures; behavioral and movement abnormalities; hypotonia; and variable dysmorphic facial features. We show that these SPTBN1 variants lead to effects that affect βII-spectrin stability, disrupt binding to key molecular partners, and disturb cytoskeleton organization and dynamics. Our studies define SPTBN1 variants as the genetic basis of a neurodevelopmental syndrome, expand the set of spectrinopathies affecting the brain and underscore the critical role of βII-spectrin in the central nervous system. SPTBN1 mutations cause a neurodevelopmental syndrome characterized by intellectual disability, language and motor delays, autism, seizures and other features. The variants disrupt βII-spectrin function and disturb cytoskeletal organization and dynamics.

SPTBN1 encodes βII-spectrin, the ubiquitously expressed member of the β-spectrin family that forms micrometer-scale networks associated with plasma membranes. βII-spectrin is abundantly expressed in the brain, where it is essential for neuronal development and connectivity. Mice deficient in neuronal βII-spectrin expression have defects in cortical organization, global developmental delay, dysmorphisms, and behavioral deficiencies of corresponding severity. These phenotypes, while less severe, are observed in haploinsufficient animals, suggesting that individuals carrying heterozygous variants in this gene may also present with measurable compromise of neural development and function. Here we report the identification of heterozygous SPTBN1 variants in 29 individuals who present with global developmental, language and motor delays, mild to severe intellectual disability, autistic features, seizures, behavioral and movement abnormalities, hypotonia, and variable dysmorphic facial features. We show that these SPTBN1 variants lead to loss-of-function, gain-of-function, and dominant negative effects that affect protein stability, disrupt binding to key protein partners, and disturb cytoskeleton organization and dynamics. Our studies define the genetic basis of this new neurodevelopmental syndrome, expand the set of spectrinopathies affecting the brain and neural development, and underscore the critical role of βII-spectrin in the central nervous system.

The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The extensive and standardized phenotype screens allow the identification of new potential models for human disease through cross-species comparison by computing the similarity between the phenotypes observed in the mutant mice and the human phenotypes associated to their orthologous loci in Mendelian disease. Here, we present an update on the novel disease models available from the most recent data release (DR10.0), with 5861 mouse genes fully or partially phenotyped and a total number of 69,982 phenotype calls reported. With approximately one-third of human Mendelian genes with orthologous null mouse phenotypes described, the range of available models relevant for human diseases keeps increasing. Among the breadth of new data, we identify previously uncharacterized disease genes in the mouse and additional phenotypes for genes with existing mutant lines mimicking the associated disorder. The automated and unbiased discovery of relevant models for all types of rare diseases implemented by the IMPC constitutes a powerful tool for human genetics and precision medicine.

Introduction The pathophysiology of PD (Parkinson's disease) has been related to the ubiquitin proteasome system and oxidative stress. Parkin acts as ubiquitin ligase on several substrates. Because genetic variants often have different frequencies across populations, population specific analyses are necessary to complement and validate results from genome-wide association studies. Methods We carried out an association study with genes coding for parkin substrates and cellular stress components in the Galician population (Northern Spain). SNCA and MAPT SNPs were also analyzed. We studied 75 SNPs in a discovery sample of 268 PD patients and 265 controls from Galicia. A replication sample of 271 patients and 260 controls was recruited from Mexico City. Results We observed significant association between PD and SNPs in MAPT. Nominal p-values < 0.05 were obtained in the Galician cohort for SNPs in SYT11, coding for synaptotagmin XI. These results were replicated in the Mexican sample. Discussion The associated markers lie within a ~ 140 kb strong linkage disequilibrium segment that harbors several candidate genes, including SYT11. SNPs from the GBA-SYT11-RAB25 region have been previously associated with PD, however the functionally relevant variants remain unknown. Our data support a likely role of genetic factors within 1q22 in PD susceptibility.

The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied. Electronic supplementary material The online version of this article (10.1007/s10592-018-1072-9) contains supplementary material, which is available to authorized users.

Introduction: Variant detection protocols for clinical next-generation sequencing (NGS) need application-specific optimization. Our aim was to analyze the performance of single nucleotide variant (SNV) and copy number (CNV) detection programs on an NGS panel for a rare disease. Methods: Thirty genes were sequenced in 83 patients with hereditary spastic paraplegia. The variant calls obtained with LifeScope, GATK UnifiedGenotyper and GATK HaplotypeCaller were compared with Sanger sequencing. The calling efficiency was evaluated for 187 (56 unique) SNVs and indels. Five multiexon deletions detected by multiple ligation probe assay were assessed from the NGS panel data with ExomeDepth, panelcn.MOPS and CNVPanelizer software. Results: There were 48/51 (94%) SNVs and 1/5 (20%) indels consistently detected by all the calling algorithms. Two SNVs were not detected by any of the callers because of a rare reference allele, and one SNV in a low coverage region was only detected by two algorithms. Regarding CNVs, ExomeDepth detected 5/5 multi-exon deletions, panelcn.MOPs 4/5 and only 3/5 deletions were accurately detected by CNVPanelizer. Conclusions: The calling efficiency of NGS algorithms for SNVs is influenced by variant type and coverage. NGS protocols need to account for the presence of rare variants in the reference sequence as well as for ambiguities in indel calling. CNV detection algorithms can be used to identify large deletions from NGS panel data for diagnostic applications; however, sensitivity depends on coverage, selection of the reference set and deletion size. We recommend the incorporation of several variant callers in the NGS pipeline to maximize variant detection efficiency.

The field of medical genomics involves translating high throughput genetic methods to the clinic, in order to improve diagnostic efficiency and treatment decision making. Technical questions related to sample enrichment, sequencing methodologies and variant identification and calling algorithms, still need careful investigation in order to validate the analytical step of next generation sequencing techniques for clinical applications. However, the main foreseeable challenge will be interpreting the clinical significance of the variants observed in a given patient, as well as their significance for family members and for other patients.Every step in the variant interpretation process has limitations and difficulties, and its quote of contribution to false positive and false negative results. There is no single piece of evidence enough on its own to make firm conclusions on the pathogenicity and disease causality of a given variant.A plethora of automated analysis software tools is being developed that will enhance efficiency and accuracy. However a risk of misinterpretation could derive from biased biorepository content, facilitated by annotation of variant functional consequences using previous datasets stored in the same or linked repositories. In order to improve variant interpretation and avoid an exponential accumulation of confounding noise in the medical literature, the use of terms in a standard way should be sought and requested when reporting genetic variants and their consequences. Generally, stepwise and linear interpretation processes are likely to overrate some pieces of evidence while underscoring others. Algorithms are needed that allow a multidimensional, parallel analysis of diverse lines of evidence to be carried out by expert teams for specific genes, cellular pathways or disorders.

Background: Migraine is a prevalent neurological disorder with a complex genetic background characterized by recurrent episodes of headache. The disease is subclassified into migraine with aura (MA) and migraine without aura (MO). Many association studies have been performed to date to identify genetic risk variants for common migraine, most of them focusing on selected candidate genes, with variable and often inconsistent results. Recently, a clinic-based genome-wide association study for migraine reported a functionally relevant risk variant (SNP rs1835740), involved in glutamate homeostasis, which showed a significant association with MA. We aimed to replicate this finding in a clinic-based study of a Spanish cohort with MA and MO patients. Methods: We genotyped SNP rs1835740 in a Spanish sample of 1521 patients and 1379 screened controls and performed a case-control association study. Conclusion: No association was found between the assayed SNP and any of the clinical groups considered.

Spinocerebellar ataxia 36 has been recently described in Japanese families as a new type of spinocerebellar ataxia with motor neuron signs. It is caused by a GGCCTG repeat expansion in intron 1 of NOP56 . Family interview and document research allowed us to reconstruct two extensive, multigenerational kindreds stemming from the same village (Costa da Morte in Galicia, Spain), in the 17th century. We found the presence of the spinocerebellar ataxia 36 mutation co-segregating with disease in these families in whom we had previously identified an ∼0.8 Mb linkage region to chromosome 20 p. Subsequent screening revealed the NOP56 expansion in eight additional Galician ataxia kindreds. While normal alleles contain 5–14 hexanucleotide repeats, expanded alleles range from ∼650 to 2500 repeats, within a shared haplotype. Further expansion of repeat size was frequent, especially upon paternal transmission, while instances of allele contraction were observed in maternal transmissions. We found a total of 63 individuals carrying the mutation, 44 of whom were confirmed to be clinically affected; over 400 people are at risk. We describe here the detailed clinical picture, consisting of a late-onset, slowly progressive cerebellar syndrome with variable eye movement abnormalities and sensorineural hearing loss. There were signs of denervation in the tongue, as well as mild pyramidal signs, but otherwise no signs of classical amyotrophic lateral sclerosis. Magnetic resonance imaging findings were consistent with the clinical course, showing atrophy of the cerebellar vermis in initial stages, later evolving to a pattern of olivo-ponto-cerebellar atrophy. We estimated the origin of the founder mutation in Galicia to have occurred ∼1275 years ago. Out of 160 Galician families with spinocerebellar ataxia, 10 (6.3%) were found to have spinocerebellar ataxia 36, while 15 (9.4%) showed other of the routinely tested dominant spinocerebellar ataxia types. Spinocerebellar ataxia 36 is thus, so far, the most frequent dominant spinocerebellar ataxia in this region, which may have implications for American countries associated with traditional Spanish emigration. * Abbreviations : SCA : spinocerebellar ataxia SNP : single nucleotide polymorphism TP-PCR : triplet repeat primed polymerase chain reaction