THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. An aggregated data platform for genome sequencing data created by a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data set provided on this website spans 61,486 unrelated individuals sequenced as part of various disease-specific and population genetic studies. They have removed individuals affected by severe pediatric disease, so this data set should serve as a useful reference set of allele frequencies for severe disease studies. All of the raw data from these projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects. They ask that you not publish global (genome-wide) analyses of these data until after the ExAC flagship paper has been published, estimated to be in early 2015. If you''re uncertain which category your analyses fall into, please email them. The aggregation and release of summary data from the exomes collected by the Exome Aggregation Consortium has been approved by the Partners IRB (protocol 2013P001477, Genomic approaches to gene discovery in rare neuromuscular diseases).

The Exome Aggregation Consortium (ExAC) is a coalition of investigators seeking to aggregate and harmonize exome sequencing data from a variety of large-scale sequencing projects, and to make summary data available for the wider scientific community. The data pertains to unrelated individuals sequenced as part of various disease-specific and population genetic studies and serves as a reference set of allele frequencies for severe disease studies. This collection references gene information.

Seven SNPs in APOE and TOMM40 (indicated by * of the SNP IDs) and 29 novel SNPs reaching exome-wide significance (p < 3.0 x 10−7, Bonferroni-corrected cutoff of p < 0.05 / # tests): Population minor allele frequency (MAF) in cases and controls, MAF in controls processed by Illumina or NimbleGen exome capture kit, and MAF in Non-Finish European (NFE) cohort of the ExAC database (http://exac.broadinstitute.org/).

List of ClinVar submitters. The analysis presented in this work were based on ClinVar entries from the listed submitters only. The submitter names are as they appear in the ClinVar XML file and may include multiple names from the same institution. (XLSX 9 kb)

Frequencies of NMDpositive variants in gain-of-function arrhythmia/cardiomyopathy genes in ExAC. (XLSX 31 kb)

The genes reported in this study include TYR, OCA2, TYRP1, SLC45A2, SLC24A5, LRMDA, KITLG, POMC, SLC24A4, TPCN2. Common alleles indicated in S1 Table to S3 Table are also included. (XLSX)

Non-traditional data signals from social media and employment platforms for EXAC stock analysis

List of the all NMDpositive variants with allele frequencies greater than 0.01% in the set of genes described in Table 2. Known and suspected founder mutations are indicated. PCD primary ciliary dyskinesia. (XLSX 48 kb)

This dataset contains the predicted prices of the asset Exac over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Protein-Protein, Genetic, and Chemical Interactions for Fragoza R (2019):Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at?>?1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Minor allele frequency in the African American HCM cohort, African American controls and individuals with African ancestry from the ExAc Database.

Genomic coordinates from NCBI Build 37.1 (hg19) and dbSNP refSNP (rs) identifiers from build 150 or submitted SNP (ss) numbers are provided. Sample counts and/or minor allele frequency (MAF) for MS patients, healthy controls and the Exome Aggregation Consortium (ExAC) database are given. Estimated effect on protein function was assessed with the Combined Annotation Dependent Depletion (CADD) phred-scale scores v1.4. n/a, not available.

Summary of population frequencies of nsSNVs alleles of transglutaminases in the ExAC database covering 60,706 individuals.

Table S2. Filtered variants detected in three or more patients. Chromosome, start, end - genome coordinates of variant; Ref/Alt - reference/alternative variant sequence. GT - genotype detected in a given sample (0 - reference, 1-alternative). DP - number of high quality reads at a given position in a given patient. Func.refGene - location of variant relative to gene. Gene - symbol of a gene the given variant maps to, or names of genes the given variants maps in between. GeneDetail.refGene - refSeq gene ID or distance to nearest gene given in the “Gene” column. ExAC_XXX - frequency of the alternative variant in XXX population according to ExAC database. 1000g_all/eur - variant frequency in the 1000 Genomes Project database (total/European). esp6500siv2_all - variant frequency according to National Heart, Lung, and Blood Institute GO Exome Sequencing Project. SIFT/Polyphen2/LRT/FATHMM/RadialSVM “_pred” - prediction of variant impact on protein structure: B-benign, N-neutral, T-tolerated, D-deleterious. ICGC_Id - variant ID in ICGC database (known cancer-related variants). Heterozygous variants are marked orange, homozygous are marked red. (XLSX 34 kb)

Mutation Taster (http://www.mutationtaster.org) was used to predict potential contributions of individual mutations to disease. Allele frequencies were evaluated using the ExAc database (http://exac.broadinstitute.org). N/A; not appropriate. The score of Mutation Taster is taken from the Grantham Matrix for amino acid substitutions and reflects the physicochemical difference between the original and the mutated amino acid. It ranges from 0.0 to 215 but does not provide a value for amino acid insertions/deletions. A higher Grantham score is indicative of a greater difference in chemical properties between two amino acids and can indicate a stronger effect on protein structure and function.Gene mutation in renal coloboma syndrome.

The carrier rates of Mendelian disorders were estimated using ExAC dataset. Child-hood cardiomyopathy (MIM no description) and Usher syndrome type 1J (USH1J) (#614869) were detected in ExAC but not in 1000G and NHLBI. ExAC populations are largely divided into six races: African, Latino, European (non-Finnish), European (Finnish), South Asian, East Asian, and Other.

Next-generation sequencing is effective for the molecular diagnosis of genetic diseases. However, the identification of the clinical significance of synonymous variants remains a challenge. Our previous study showed that some synonymous variants in ATP7B gene produced splicing disruptions, leading to Wilson disease (WD). To test the hypothesis that synonymous variants of ATP7B cause abnormal splicing by disrupting authentic splice sites or splicing regulatory elements, we used computational tools and minigene assays to characterize 253 naturally occurring ATP7B gene synonymous variants in this study. Human Splicing Finder (HSF) and ESE Finder 3.0 were used to predict the impact of these rare synonymous variants on pre-mRNA splicing. Then, we cloned 14 different wild-type Minigene_ATP7B_ex constructs for in vitro minigene assay, including 16 exons of ATP7B gene. After computational prediction, 85 candidate variants were selected to be introduced into the corresponding Minigene_ATP7B_ex constructs for splicing assays. Using this two-step procedure, we demonstrated that 11 synonymous variants in ExAc database (c.1620C>T, c.3888C>T, c.1554C>T, c.1677C>T, c.1830G>A, c.1875T>A, c.2826C>A, c.4098G>A, c.2994C>T, c.3243G>A, and c.3747G>A) disrupted RNA splicing in vitro, and two (c.1620C>T and c.3243G>A) of these caused a complete exon skipping. The results not only provided a reliable experimental basis for the genetic diagnosis of WD patients but also offered some new insights into the pathogenicity of synonymous variants in genetic diseases.

Control frequency refers to the allele frequency of the same variant in the subjects included in the ExAC database (60,706 unrelated individuals). Progenitor phenotype refers only to the progenitor carrying the alteration. Hg19 assembly. F: female; M: male; Mat: maternal; Pat: paternal; NA: not available.

The proportion of sdSNVs with a higher DAF in PJL was greater in all pairwise population comparisons. On the other hand, from the comparison of ExAC_SAS with other populations, the proportion of sdSNVs with higher DAF in SAS was greater than NFE (Non-Finnish Europeans) only, while, it was less than AMR (Americans), AFR (Africans), EAS (East Asians), and FIN (Finnish) populations.

Recalculation of the Supplementary Table 2 (doi:10.1371/journal.pcbi.1004647.s002) of the journal article "The Characteristics of Heterozygous Protein Truncating Variants in the Human Genome" by Bartha and Rausell published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1004647). Probabilities in this dataset were computed using human variation data from the Exome Aggregation Consortium (http://exac.broadinstitute.org/).

Methods described in that article is relevant for this dataset. All author and affiliation information in that article is relevant for this dataset.

Credit for the original human variation data is for the Exome Aggregation Consortium (http://exac.broadinstitute.org/, doi:10.1038/nature19057).