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We performed a whole-genome association study of human immunodeficiency virus type 1 (HIV-1) set point among a cohort of African Americans (n=515), and an intronic single-nucleotide polymorphism (SNP) in the HLA-B gene showed one of the strongest associations. We use a subset of patients to demonstrate that this SNP reflects the effect of the HLA-B*5703 allele, which shows a genome-wide statistically significant association with viral load set point (P=5.6×10−10). These analyses therefore confirm a member of the HLAB* 57 group of alleles as the most important common variant that influences viral load variation in African Americans, which is consistent with what has been observed for individuals of European ancestry, among whom the most important common variant is HLA-B*5701.

David Goldstein, Mohamad Mikati and colleagues report identification of de novo mutations in ATP1A3 in alternating hemiplegia of childhood, which is a rare neurodevelopmental syndrome characterized by recurrent hemiplegic episodes and distinct neurologic manifestations. Alternating hemiplegia of childhood (AHC) is a rare, severe neurodevelopmental syndrome characterized by recurrent hemiplegic episodes and distinct neurological manifestations. AHC is usually a sporadic disorder and has unknown etiology. We used exome sequencing of seven patients with AHC and their unaffected parents to identify de novo nonsynonymous mutations in ATP1A3 in all seven individuals. In a subsequent sequence analysis of ATP1A3 in 98 other patients with AHC, we found that ATP1A3 mutations were likely to be responsible for at least 74% of the cases; we also identified one inherited mutation in a case of familial AHC. Notably, most AHC cases are caused by one of seven recurrent ATP1A3 mutations, one of which was observed in 36 patients. Unlike ATP1A3 mutations that cause rapid-onset dystonia-parkinsonism, AHC-causing mutations in this gene caused consistent reductions in ATPase activity without affecting the level of protein expression. This work identifies de novo ATP1A3 mutations as the primary cause of AHC and offers insight into disease pathophysiology by expanding the spectrum of phenotypes associated with mutations in ATP1A3 .

Alternating hemiplegia of childhood (AHC) is a rare neurodevelopment disorder that is typically characterized by debilitating episodic attacks of hemiplegia, seizures, and intellectual disability. Over 85% of individuals with AHC have a de novo missense variant in ATP1A3 encoding the catalytic α3 subunit of neuronal Na+/K+ ATPases. The remainder of the patients are genetically unexplained. Here, we used next-generation sequencing to search for the genetic cause of 26 ATP1A3-negative index patients with a clinical presentation of AHC or an AHC-like phenotype. Three patients had affected siblings. Using targeted sequencing of exonic, intronic, and flanking regions of ATP1A3 in 22 of the 26 index patients, we found no ultra-rare variants. Using exome sequencing, we identified the likely genetic diagnosis in 9 probands (35%) in five genes, including RHOBTB2 (n = 3), ATP1A2 (n = 3), ANK3 (n = 1), SCN2A (n = 1), and CHD2 (n = 1). In follow-up investigations, two additional ATP1A3-negative individuals were found to have rare missense SCN2A variants, including one de novo likely pathogenic variant and one likely pathogenic variant for which inheritance could not be determined. Functional evaluation of the variants identified in SCN2A and ATP1A2 supports the pathogenicity of the identified variants. Our data show that genetic variants in various neurodevelopmental genes, including SCN2A, lead to AHC or AHC-like presentation. Still, the majority of ATP1A3-negative AHC or AHC-like patients remain unexplained, suggesting that other mutational mechanisms may account for the phenotype or that cases may be explained by oligo- or polygenic risk factors.

The Undiagnosed Diseases Network, an NIH-funded network of seven U.S. sites, accepted 601 patients for evaluation over a 20-month period. Of the 382 patients who had a complete evaluation, 132 (35%) received a diagnosis; 15 of the diagnoses (11%) were made by clinical review alone and 98 (74%) by exome or genome sequencing.

The epilepsies are a clinically heterogeneous group of neurological disorders. Despite strong evidence for heritability, genome-wide association studies have had little success in identification of risk loci associated with epilepsy, probably because of relatively small sample sizes and insufficient power. We aimed to identify risk loci through meta-analyses of genome-wide association studies for all epilepsy and the two largest clinical subtypes (genetic generalised epilepsy and focal epilepsy). We combined genome-wide association data from 12 cohorts of individuals with epilepsy and controls from population-based datasets. Controls were ethnically matched with cases. We phenotyped individuals with epilepsy into categories of genetic generalised epilepsy, focal epilepsy, or unclassified epilepsy. After standardised filtering for quality control and imputation to account for different genotyping platforms across sites, investigators at each site conducted a linear mixed-model association analysis for each dataset. Combining summary statistics, we conducted fixed-effects meta-analyses of all epilepsy, focal epilepsy, and genetic generalised epilepsy. We set the genome-wide significance threshold at p<1·66 × 10−8. We included 8696 cases and 26 157 controls in our analysis. Meta-analysis of the all-epilepsy cohort identified loci at 2q24.3 (p=8·71 × 10−10), implicating SCN1A, and at 4p15.1 (p=5·44 × 10−9), harbouring PCDH7, which encodes a protocadherin molecule not previously implicated in epilepsy. For the cohort of genetic generalised epilepsy, we noted a single signal at 2p16.1 (p=9·99 × 10−9), implicating VRK2 or FANCL. No single nucleotide polymorphism achieved genome-wide significance for focal epilepsy. This meta-analysis describes a new locus not previously implicated in epilepsy and provides further evidence about the genetic architecture of these disorders, with the ultimate aim of assisting in disease classification and prognosis. The data suggest that specific loci can act pleiotropically raising risk for epilepsy broadly, or can have effects limited to a specific epilepsy subtype. Future genetic analyses might benefit from both lumping (ie, grouping of epilepsy types together) or splitting (ie, analysis of specific clinical subtypes). International League Against Epilepsy and multiple governmental and philanthropic agencies.

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform in-depth analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development. The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale case-based diagnostic analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser ( https://dbmi-bgm.github.io/udn-browser/ ). These results show that case-level diagnostic efforts should be supplemented by a joint genomic analysis across cohorts. Using well-calibrated statistical methods the authors jointly analyze Undiagnosed Diseases Network genomes, identifying known and novel disease genes. Software is publicly available to support future cross-cohort rare disease discovery efforts.

Short tandem repeats (STRs) are widely distributed across the human genome and are associated with numerous neurological disorders. However, the extent that STRs contribute to disease is likely under-estimated because of the challenges calling these variants in short read next generation sequencing data. Several computational tools have been developed for STR variant calling, but none fully address all of the complexities associated with this variant class. Here we introduce LUSTR which is designed to address some of the challenges associated with STR variant calling by enabling more flexibility in defining STR loci, allowing for customizable modules to tailor analyses, and expanding the capability to call somatic and multiallelic STR variants. LUSTR is a user-friendly and easily customizable tool for targeted or unbiased genome-wide STR variant screening that can use either predefined or novel genome builds. Using both simulated and real data sets, we demonstrated that LUSTR accurately infers germline and somatic STR expansions in individuals with and without diseases. LUSTR offers a powerful and user-friendly approach that allows for the identification of STR variants and can facilitate more comprehensive studies evaluating the role of pathogenic STR variants across human diseases.

Background The majority of undiagnosed diseases manifest with objective findings that warrant further investigation. The Undiagnosed Diseases Network (UDN) receives applications from patients whose symptoms and signs have been intractable to diagnosis; however, many UDN applicants are affected primarily by subjective symptoms such as pain and fatigue. We sought to characterize presenting symptoms, referral sources, and demographic factors of applicants to the UDN to identify factors that may determine application outcome and potentially differentiate between those with undiagnosed diseases (with more objective findings) and those who are less likely to have an undiagnosed disease (more subjective symptoms). Methods We used a systematic retrospective review of 151 consecutive Not Accepted and 50 randomly selected Accepted UDN applications. The primary outcome was whether an applicant was Accepted, or Not Accepted, and, if accepted, whether or not a diagnosis was made. Objective and subjective symptoms and information on prior specialty consultations were collected from provider referral letters. Demographic data and decision data on network acceptance were gathered from the UDN online portal. Results Fewer objective findings and more subjective symptoms were found in the Not Accepted applications. Not Accepted referrals also were from older individuals, reported a shorter period of illness, and were referred to the UDN by their primary care physicians. All of these differences reached statistical significance in comparison with Accepted applications. The frequency of subspecialty consults for diagnostic purposes prior to UDN application was similar in both groups. Conclusions The preponderance of subjective and lack of objective findings in the Not Accepted applications distinguish these from applicants that are accepted for evaluation and diagnostic efforts through the UDN. Not Accepted applicants are referred primarily by their primary care providers after multiple specialist consultations fail to yield answers. Distinguishing between patients with undiagnosed diseases with objective findings and those with primarily subjective findings can delineate patients who would benefit from further diagnostic processes from those who may have functional disorders and need alternative pathways for management of their symptoms. Trial registration clinicaltrials.gov NCT02450851 , posted May 21st 2015.

A strong association between the HLA-B*1502 allele and SJS and TEN induced by carbamazepine has been shown. This study involving Europeans implicates a different HLA allele, HLA-A*3101, in conferring susceptibility to a broad range of carbamazepine-induced reactions. Carbamazepine is one of the most commonly prescribed drugs for the treatment of epilepsy, as well as trigeminal neuralgia and bipolar disorder. A minority of treated persons have hypersensitivity reactions that vary in prevalence and severity, 1 with some forms associated with substantial morbidity and mortality. The mildest form, maculopapular exanthema, occurs in 5 to 10% of treated persons of European ancestry and resolves spontaneously after drug discontinuation. More severe reactions, such as the hypersensitivity syndrome, are associated with mortality of up to 10% 2 and include symptoms such as rash, fever, eosinophilia, hepatitis, and nephritis. The most severe reactions, such as . . .

The Epilepsy Genetics (EPIGEN) Consortium was established to undertake genetic mapping analyses with augmented statistical power to detect variants that influence the development and treatment of common forms of epilepsy. We examined common variations across 279 prime candidate genes in 2717 case and 1118 control samples collected at four independent research centres (in the UK, Ireland, Finland, and Australia). Single nucleotide polymorphism (SNP) and combined set-association analyses were used to examine the contribution of genetic variation in the candidate genes to various forms of epilepsy. We did not identify clear, indisputable common genetic risk factors that contribute to selected epilepsy subphenotypes across multiple populations. Nor did we identify risk factors for the general all-epilepsy phenotype. However, set-association analysis on the most significant p values, assessed under permutation, suggested the contribution of numerous SNPs to disease predisposition in an apparent population-specific manner. Variations in the genes KCNAB1, GABRR2, KCNMB4, SYN2, and ALDH5A1 were most notable. The underlying genetic component to sporadic epilepsy is clearly complex. Results suggest that many SNPs contribute to disease predisposition in an apparently population-specific manner. However, subtle differences in phenotyping across cohorts, combined with a poor understanding of how the underlying genetic component to epilepsy aligns with current phenotypic classifications, might also account for apparent population-specific genetic risk factors. Variations across five genes warrant further study in independent cohorts to clarify the tentative association.