dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico prediction softwares VarSome https://varsome. com/ Annotation tool that classifies the genetic variant according to ACMG criteria. The verdict arises from a vast quantity of accurate curated data such as coding impact of the variant, in vitro functional studies, allele frequency, previous publications and other databases analyses. PolyPhen-2 http://genetics. bwh. harvard. edu/pph2/ A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. Provean (Protein Variation Effect Analyzer) http:// provean. jcvi. org/index. php A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. SIFT https://sift. bii. a-star. edu. sg/ A tool which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein.
Mutation taster http://www. mutationtaster. org/ A tool which predicts whether an amino acid substitution, insertion or deletion has an impact on the biological function of a protein. T A B L E 3 Characteristics of common controversial GLA variants according to genetic databases and in silico prediction softwares The Japanese Fabry Database LO [5]; classic [4]; B [3]; VUS [1]; np [8]   Classic [5]; B [2]; LO [1]; np [9]  B [5]; classic [5]; LO [3]; np [3]   LO [2]; np [5]   LO [1]; np [6]   Pathogenicity according to general databases ClinVar VUS [10]; LP [4]; P [2]   LB [13]; VUS [3]; B [2]   VUS [4]; LB [2]   VUS [12]; LP [2]; LB [1]   LB [6]; VUS [4]; B [1]   LOVD LB [2]; VUS [1]   LB [3]; B [2]; VUS [1]   np VUS [3]; P [1]   2 LB [2]; VUS [  Varsome user reported this variant as "likely pathogenic" in a paper supported by a functional expression study, with additional removal of both criteria BS1 and BS2 criteria.
Furthermore, p. (Ala143Thr) is classified as benign in dbFGP, with conflicting results in other databases (ClinVar, LOVD, the Japanese Fabry Database) (Table  3 ). While in silico prediction softwares (e. g. , PolyPhen-2, PROVEAN, SIFT, Mutation Taster) can help in determining the pathogenicity of missense variants, their partial reliability is well-known as confirmed with/for the five most common (likely) benign GLA variants (Table  3 ). With respect to databases, those specific for FD and curated by FD experts, appear to be most valuable for classifying GLA variant pathogenicity (Table  2 ).
| Periodically review the literature and specific databases It is important to periodically review the literature and perform a specific database search for additional evidence on pathogenicity/ benignity of variants, since with growing evidence over time, some variants originally designated VUS may be reclassified as either likely pathogenic/pathogenic or in contrast likely benign/benign and in the latter case, no longer considered to be FD-causing variants. 35, 54, 58, 59, 62, 72 However, careful consideration is necessary when using the variants classification from these databases, as they allow.
| CONCLUSION Since the publication of ACMG-AMP,  13  the approach to interpreting unknown/novel GLA variants integrates information from in vitro prediction softwares, AF and gene databases. 13 However, this traditional approach is not universally applicable for rare diseases. We recommend that physicians should be aware of potential errors in interpreting GLA variants. Misdiagnosis prolongs patient diagnostic odyssey, resulting in higher morbidity and adding unnecessary financial burden to the healthcare system. 69 Accurate interpretation benefits from the input of rare disease expert clinicians and geneticists (Table  4 ; Figure  3 ). We call for more data sharing by referral centers worldwide to increase the robustness of disease specific databases. These lessons obtained from Fabry disease contribute, with practical concepts, to better interpret the pathogenicity of allelic variants in genetic diseases for a more accurate diagnosis and effective management.
Last accessed 2020-08-04. [ ]: The number of times referenced. Abbreviations: AF, allele frequency; B, benign; dbFGP, International Fabry Disease Genotype-Phenotype Database; FD, Fabry disease; gnomAD, Genome Aggregation Database; LB, likely benign; LOVD, Leiden Open (source) Variation Database; LP, likely pathogenic; np, not provided; P, pathogenic; VUS, variant of unknown significance. queried and the time of the query. For example, the classification of the p. (Ala143Thr) variant was changed from "likely pathogenic" (on December 5th, 2019) to VUS (on January 20th, 2020) in Varsome (https://varsome. com) with the addition of "BS1" (Benign Strong criterion number 1, AF higher than expected for the disorder). It changed again from "VUS" to "pathogenic" on August 3rd, 2020, with the addition of the "PS3" (Pathogenic Strong criterion number 3), as a.
Practical recommendations for a more accurate diagnosis of Fabry disease• Combine insights from disease specific databases and in vitro prediction tools with expert clinical opinion, defining the relative weight of each ACMG criterion. • Consider the genetic variant as probably benign if its allele total or subpopulation frequency is: Higher than the overall prevalence of FD (0. 0125%), Higher than the frequency of the most common pathogenic allele. • Perform segregation analysis when additional information is required. • Review the literature and databases periodically to check whether the variant has been reclassified.
Most commonly reported types of GLA variants in the Human Gene Mutation Database (HGMD) and related Fabry disease (FD) phenotypes in hemizygous patients p. (Ala143Thr) ranges between 25% and 72% of normal, 700 61. 0% Number and % of most GLA variants commonly reported 100 200 300 400 500 600 25. 5% 8. 5% 4. 9% 0 Missense Frameshift Nonsense Splicing variants. 605 253 84 49 FD Phenotype 69 Classic / Later-onset Classic Classic Classic / Later-onset / Absence of FD F I G U R E 2 35,54ative biopsies and generally an absence of stroke/TIA in other family members carrying p. (Ala143Thr). 35,54Alpha-Gal A residual activity in males with.
Available databases and in silico prediction softwares Created by Meiji Pharmaceutical University and led by H. Sakuraba, this database lists GLA variants and the reported clinical phenotypes with references. ClinVar https://www. ncbi. nlm. nih. gov/clinvar Collects evidence-supported interpretations of clinical significance of variants for FD submitted by clinical testing laboratories, researchers, other databases. Leiden Open (source) Variation Database (LOVD) Displays gene variants. https://www. lovd. nl/ OMIM ® (Online Mendelian Inheritance in Man) https:// Contains information on all known Mendelian disorders and over 15,000 genes, focusing omim. org/ on the relationship between phenotype and genotype. International Fabry Disease Genotype-Phenotype Combines data from databases such as the Human Gene Mutation Database (HGMD) Database (dbFGP) http://dbfgp.
org/dbFgp/fabry and The Japanese Fabry Database, diagnostic and clinical evaluations of patients with data from peer reviewed publications and input from expert FD researchers and care teams. The database provides information of the associated phenotype for a given variant. The Japanese Fabry Database http://fabry-database. org/mutants/ Name DescriptionDatabasesGenome Aggregation Database (gnomAD) https:// gnomad. broadinstitute. org/ Includes aggregated exome and genome sequencing data. Useful to compare the frequency of a variant in the general population or in an ethnic group against the disease prevalence or against the frequency of the most common pathogenic variant. A GLA variant is not considered disease-causing if its frequency is higher than FD prevalence or the reported most frequent variant (Popmax Filtering AF).
Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects.
Further expanding the ALS-FTD spectrum, a genetic correlation with progressive supranuclear palsy (PSP) has been described  20. Shared pathogenic mechanisms between ALS and other neurodegenerative diseases, including common diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), can further reveal ALS pathophysiology and inform new therapeutic strategies. Here, we combine new and existing individual-level genotype data in the largest GWAS of ALS to date. We present a comprehensive screen for pathogenic rare variants and short tandem repeat (STR) expansions as well as regulatory effects observed in brain cortex-derived RNA sequencing (RNA-seq) and methylation datasets to prioritize causal genes within ALS-risk loci. Furthermore, we reveal similarities and differences between ALS and other neurodegenerative diseases as well as the biological processes in disease-relevant tissues and cell types that affect ALS risk.
Rare variant gene-based association analyses in ALS. To assess a general pattern of underlying architectures that link associated SNPs to causal genes, we first tested for annotation-specific enrichment using stratified LDSC. This revealed that 5′ UTR regions as well as coding regions in the genome and those annotated as conserved were most enriched for ALS-associated SNPs (Extended Data Fig. 2 ). Subsequently, we investigated how rare, coding variants contributed to ALS risk by generating a whole-genome sequencing (WGS) dataset of patients with ALS (n = 6,538) and control participants (n = 2,415), which is a subset of the common variant GWAS cohort. The exome-wide association analysis included transcript-level rare variant burden testing for different models of allele-frequency thresholds and variant annotations (Methods).
Although power was limited, this is compatible with a scenario in which the genetic risk of ALS in these patients is a sum of rare variants in ALS genes and other (common) genetic variation. Gene prioritization shows locus-specific underlying architectures. To assess whether rare variant associations could drive the common variant signals at the 15 genome-wide significant loci, we combined the common and rare variant analyses to prioritize genes within these loci. The SNP effects on gene expression were assessed by summary-based Mendelian randomization (MR) (SMR) in blood (eQTLGen  23  , n = 31,648) and a new brain cortex-derived eQTL dataset (MetaBrain  24  , n = 2,970). Finally, we analyzed methylation quantitative trait loci (mQTL) by SMR in blood-derived (n = 2,082) and brain-derived (n = 522) mQTL datasets  [25] [26] [27].
e. , standard error of the effect estimate. calculated using MAGMA  32  with gene expression patterns from the Genotype-Tissue Expression (GTEx) project (version 8) in a gene set enrichment analysis using FUMA  33. We observed a significant enrichment in genes expressed in brain tissues across multiple brain regions but not in peripheral nervous tissue or muscle. Whereas this pattern roughly resembled the enrichments observed in PD and psychiatric traits, it was strikingly different from that reported  31  and observed in AD in which blood, lung and spleen were mostly enriched, resembling the pattern observed in multiple sclerosis, which is a typical immune-mediated brain disease (Fig. 4a  and full results in Supplementary Fig. 16  and Extended Data Fig. 8a ). We subsequently queried single-cell RNA-seq datasets of humanderived brain samples to further specify brain-specific enriched cell types using the cell type analysis module in FUMA  34.
Brain-specific coexpression networks improve detection of ALS-relevant pathways. To determine which processes were mostly enriched in ALS, we performed enrichment analyses that combined gene-based association statistics with gene coexpression patterns obtained from either multi-tissue transcriptome datasets  35  or RNA-seq data from brain cortex samples (MetaBrain 24 ). To validate this approach, we first tested for enrichment of human phenotype ontology (HPO) terms that are linked to well-established disease genes in the Online Mendelian Inheritance in Man (OMIM) and Orphanet catalogs. Using the multi-tissue coexpression matrix, we found no enriched HPO terms after Bonferroni correction for multiple testing. Using the brain-specific coexpression matrix, however, we found a strong enrichment of HPO terms that are related to ALS or neurodegenerative diseases in general, including 'cerebral cortical atrophy' (P = 1.
8 × 10 -8 ), 'abnormal nervous system electrophysiology' (P = 4. 1 × 10 -7 ) and 'distal amyotrophy' (P = 8. 6 × 10 -7 ; full list in Supplementary Table  22 ). In general, HPO terms in the neurological branch ('abnormality of the nervous system') showed an increase in enrichment statistics in ALS when using the brain-specific coexpression matrix compared to the multi-tissue dataset (Extended Data Fig. 10 ), which illustrates the benefit of the brain-specific coexpression matrix. Subsequently, we tested for enriched biological processes using reactome and gene ontology terms. Again, using the multi-tissue expression profiles, we found that no reactome annotations were enriched. Leveraging the brain-specific coexpression networks, we identified vesicle-mediated transport ('membrane trafficking' , P = 4.
Methods Genome-wide association study. Data description. We obtained individual genotype-level data for all individuals in the previously published GWAS of ALS in European ancestries  11, 14  and publicly available control datasets including 120,971 controls genotyped on Illumina platforms. Additionally, 6,374 cases and 22,526 controls were genotyped on the Illumina OmniExpress and Illumina GSA arrays. Details for each cohort are provided in Supplementary Table  1. All patients with ALS were diagnosed and ascertained through specialized MND clinics where they were diagnosed with ALS according to the (revised) El Escorial Criteria 56 by neurologists specialized in motor neuron diseases. Whole-blood samples were drawn for DNA isolation, which were specifically collected for ongoing case-control studies of ALS.
Both sequencing sets used PCR-free library preparation. Samples were also genotyped on the Illumina 2. 5M array. Sequencing data were then aligned to GRCh37 using the Isaac Aligner, and variants were called using the Isaac variant caller; both the aligner and caller are standard to Illumina's aligning and calling pipeline. Full details of individual-and variant-level quality control are described in the Supplementary Note. Genic burden association analyses. To aggregate rare variants in a genic burden test framework, we used a variety of variant filters to allow for different genetic architectures of ALS-associated variants per gene as we and others did previously  64, 65. In summary, variants were annotated according to allele-frequency threshold (MAF < 0. 01 or MAF < 0. 005) and predicted variant impact ('missense' , 'damaging' , 'disruptive').
'Disruptive' variants were those variants classified as frameshift, splice site, exon loss, stop gained, start loss and transcription ablation by SnpEff 66. 'Damaging' variants were missense variants predicted to be damaging by seven prediction algorithms (SIFT  67  , PolyPhen-2 (ref. 68 ), LRT  69  , MutationTaster2 (ref. 70 ), Mutations Assessor 71 and PROVEAN  72  ). 'Missense' variants were those missense variants that did not meet the 'damaging' criteria. All combinations of allele-frequency threshold and variant annotations were used to test the genic burden on a transcript level in a Firth logistic regression framework in which burden was defined as the number of variants per individual. Sex and the first 20 PCs were included as covariates. All Ensembl protein-coding transcripts for which at least five individuals had a non-zero burden were included in the analysis.
Conditional genic burden analysis. We selected for each gene the protein-coding transcripts that were the most strongly associated with ALS across all different combinations of MAF and variant-impact thresholds. For these transcripts and variants, we applied Firth logistic regression on individuals included in both the GWAS and WGS datasets (5,158 cases and 2,167 controls). To assess whether the rare variant burden association and the signal from the GWAS were conditionally independent, we subsequently included the genotype of the top associated SNP within that locus as a covariate. Short tandem repeat screen. For all individuals who had sequencing results in the HiSeq X dataset (5,392 cases, 1,795 controls), we screened all loci harboring SNPs associated with ALS meeting genome-wide significance for expansions of known and new STRs using ExpansionHunter  73  and ExpansionHunter Denovo  74.
18 ) when applied using GTEx version 7 eQTL, and it can be applied to the large relevant eQTL datasets (MetaBrain and eQTLGen) without access to individual-level genotype and gene expression data. MetaBrain is a harmonized set of 8,727 RNA-seq samples from seven regions of the central nervous system from 15 datasets, and we selected eQTL derived from the cortex region of the brain in samples of European ancestry (MetaBrain Cortex-EUR eQTL, n = 2,970 individuals, n = 6,601 RNA-seq samples) as our instrument variable  24. European-only ALS summary statistics were used as the outcome. To supplement this analysis, we also used eQTL in blood from the eQTLGen Consortium, as this is a large available eQTL resource. Samples of European ancestry in the HRS (cohort 65 of this GWAS) were used as the LD reference panel.
SNPs with MAF ≥ 1% in the HRS were included. Further SMR settings were left as default, meaning probes with at least one eQTL with P ≤ 5 × 10 -8 were included. We subsequently performed SMR using DNA mQTL data and European-only ALS summary statistics. Human prefrontal cortex and whole-blood DNA mQTL were generated as part of ongoing analyses by the Complex Disease Epigenomics Group at the University of Exeter (https://www. epigenomicslab. com/) using the Illumina EPIC HumanMethylation array that quantifies DNAm at >850,000 sites across the genome  25. The prefrontal cortex mQTL dataset was generated using DNA-methylation and SNP data from 522 individuals from the Brains for Dementia Research cohort  26  and includes 4,623,966 cis mQTL (distance between quantitative trait locus SNP and DNAm site ≤500 kb) between 1,744,102 SNPs and 43,337 DNA-methylation sites.
The whole-blood mQTL dataset was generated using DNAm and SNP data from 2,082 individuals  80  and included 30,432,023 cis mQTL between 4,030,902 SNPs and 167,854 DNA-methylation sites. mQTL reaching the significance threshold P ≤ 1 × 10 -10 were taken forward for SMR analysis as described by Hannon and colleagues  80. To map CpG sites to their putative target genes, we used the expression quantitative trait methylation results from a paired methylation and gene expression (RNA-seq) study in blood  81. For CpG sites where no expression quantitative trait methylation was present in this dataset, we used positional mapping based on the basal regulatory domains and extended regulatory domains as defined in the Genomic Regions Enrichment of Annotations Tool (GREAT)  82  , which is applied in the 'cpg_to_gene' function in the CpGtools toolkit  83.
Finally, the PRSs for all individuals in stratum 6 were calculated using the '-score' function in PLINK and normalized to zero mean and unit variance. Modifier analyses. For 6,095 of the patients with WGS and ALS, core clinical data were obtained including sex, site of onset (spinal or bulbar), age at onset (years), country of origin and survival, defined as time from disease onset to death, 23 h of continuous non-invasive ventilation per day or tracheostomy. Patients who were still alive were censored at the last date of follow-up. The genetic risk factors included SNP genotypes, PRSs, C9orf72 repeat expansion status and the number of rare coding mutations in ALS-risk genes (SOD1, TARDBP, FUS, NEK1, TBK1 and CFAP410) as obtained from WGS as described above. For survival analyses, the Cox proportional hazards mixed model from the 'coxme' package in R was used, modeling country of origin as a random effect.
Fixed-effect covariates included sex, age at onset, site of onset, GWAS stratum and PC1-PC5. Violation of the proportional hazards assumption for genotype on survival was assessed by inspecting Schoenfeld residuals. For age-at-onset analyses, we applied linear regression of age at onset on genotype including sex, site of onset, country, GWAS stratum and PC1-PC5 as covariates. Cross-trait analyses. Datasets and data preparation. GWAS summary statistics for clinically diagnosed AD  86  , PD  87  , FTD  88  , CBD  89  and PSP  20  in individuals of European ancestry were obtained. For AD, we used the clinical diagnosis as the case definition to avoid spurious genetic correlations that could have been introduced through the by-proxy design  31  , in which by-proxy cases are defined as having a parent with AD.
The option '-overlap-annot' was used in the partitioned heritability analysis to allow for overlapping SNPs between MAF bins. SNPs with MAF > 5% were included. Tissue and cell type enrichment analysis. Tissue and cell type enrichment analyses were performed using the GWAS summary statistics of the European ancestry meta-analysis and FUMA 33 software version 1. 3. 6a. FUMA performs a genic aggregation analysis of GWAS association signals to calculate gene-wise association signals using MAGMA version 1. 6 and subsequently tests whether tissues and cell types are enriched for expression of these genes. For tissue enrichment analysis, we used the GTEx version 8 reference set. FDR-corrected P-values <0. 05 across all tissues (n = 54) were considered statistically significant. For cell type enrichment analyses  34  , we used human-derived single-cell RNA-seq data on major brain cell types (GSE67835 without fetal samples  106  ), Allen Brain Atlas cell types  107  for the human-derived major neuronal subtypes and the DropViz 108 dataset for mouse-derived brain cell types across all brain regions.
We applied FDR correction for multiple testing within each expression dataset, and FDR-corrected P-values <0. 05 were considered statistically significant. Pathway enrichment analysis. We used Downstreamer software  24  to identify enriched biological pathways and processes. First, gene-based association statistics were obtained with the Pascal method  109  , which aggregates SNP association statistics including SNPs up to 10 kb upstream and downstream of a gene, accounting for LD using the non-Finnish European individuals from the 1000 Genomes Project phase 3 (ref. 110 ) as a reference. In the Downstreamer method, putative core genes are defined as those that are coexpressed with disease-associated genes and can therefore be implicated in disease. Coexpression networks are based on either a large, multi-tissue transcriptome dataset including 56,435 genes and 31,499 individuals or brain-specific RNA-seq data obtained from the MetaBrain resource.