The lack of suitable reference genomic material to enable a transparent cross-lab study of oncopanels inspired the SEQC2 Oncopanel Sequencing Working Group to develop four reference samples, sequenced with eight oncopanels at independent test laboratories with ultra-deep sequencing depth. This rich, publicly available dataset enabled performance assessment of the clinical applicability of oncopanels. In addition, this dataset present sample opportunities for developing specific and robust bioinformatics pipelines and fine-tuning parameters for more accurate variant calling, investigating ideal sequencing depth for variant calling of a given minimum VAF and variant type, and also recommending best use cases for Unique Molecular Identifier (UMI) technology.  Personalized therapy, diagnostics and prognostic prediction for cancer patients are critical components for bringing the precision medicine era from theory to practice. The advantage of emerging technologies, including the next generation sequencing, oncopanel testing, as well as the bioinformatics tools, are bringing hope to the patients and their caregivers. Physicians can guide patients to targeted therapy rather than general treatment when corresponding genomic indicators are accurately identified with these technologies. False positive variant calls from these technologies can lead to unnecessary or improper therapies as well as overestimation of biomarkers such as tumor mutational burden (TMB), a key measurement for immune checkpoint inhibitor (ICI) therapy. In recent years, the FDA has approved or cleared several cancer panels which are designed to provide physicians with clinically actionable information related to specific targets, a significant milestone for the precision medicine field. Oncopanels typically cover a small-to-large number (50–1000) of genes and can detect mutations with variant allele frequency (VAF) as low as 1% if given sufficient sequencing depth and base quality. Most pan-cancer panels are currently still for research use only. Before they can be adopted for clinical testing, a thorough understanding of their robustness, analytical characteristics, reproducibility, sensitivity, and false positive rate is greatly needed.1. Eight panel providers agreed to participant in this study: Agilent Technologies, Burning Rock Biotech, Integrated DNA Technologies, Inc., iGeneTech, Illumina Inc., QIAGEN Sciences Inc., Roche Sequencing Solutions Inc., and Thermo Fisher Scientific. In order to evaluate the performance of oncopanels in a comprehensive and fair manner, we formed the Oncopanel Sequencing Working Group under the umbrella of the Sequencing Quality Control Phase II (SEQC2) consortium. The working group contained 151 members from 100 institutes, government agencies and private companies across 14 countries. The working group mainly focused on assessing the oncopanel performance using three key metrics: sensitivity, false positive rate, and reproducibility. We developed four related reference samples with a large number of high-confident small variant positive and negatives positions to serve as known content for the performance assessment2. The four reference samples were distributed to the eight participating panel providers identified previously for library preparation, sequencing, and variant calling. We asked each panel provider to recruit at least three independent laboratories with which they were remarkably familiar to processing their panels. Each lab made four panel libraries for each reference sample following the harmonized experimental protocols developed by the panel providers accordingly. The libraries from the same panel were sequenced on various sequencing platforms, including Illumina HiSeq 2500, NovaSeq, NextSeq and ThermoFisher IonTorrent S5. Each panel provider performed the downstream analysis using their recommended or in-house bioinformatics pipelines for variant calling, but blinded to any of the known content. Variant calling results were submitted in VCF format, along with their reporting regions and allowlist/blocklist, to an independent group for performance evaluation. In addition to the primary goal of assessing the performance and the clinical applicability of oncopanels, the dataset also holds its value for: i) developing specific bioinformatics pipelines and fine-tuning parameters for more accurate variant calling for a specific panel, ii) developing robust bioinformatics pipelines that can be applied on multiple panels with promising results, iii) investigating ideal sequencing depth for variant calling of a given minimum VAF and certain variant type , and iv) making the best use of UMI technology.We proposed a multi-lab cross-oncopanel study to assess the performance and the clinical applicability of oncopanels3. Sample B is a cell line derived from a normal male individual available at Agilent. Sample C is the 1:1 dilution of Sample A and Sample B. Sample Spike-in is a mixture of Sample B with 5% AcroMetrix hotspot synthetic controls4. There were eight oncopanels included in this study: Agilent Custom Comprehensive Cancer Panel v2 (AGL), Burning Rock DX OncoScreen Plus (BRP), Integrated DNA Technologies xGen Pan-Cancer Panel (IDT), iGeneTech AIOnco-seq (IGT), Illumina TruSight Tumor 170 (ILM), QIAGEN Comprehensive cancer panel (QGN), Roche SeqCap EZ Choice custom PHC Panel (ROC), and Thermo Fisher Oncomine Comprehensive Assay v3 (TFS)  Fig. . Sample 3. Over 42 K small variants are identified with high confidence in the defined regions of over 22 million bases2.Sample A is composed of an equal mass pooling of 10 gDNA samples prepared from Agilent’s Universal Human Reference RNA (UHRR) cancer cell linesSample B is a gDNA sample from a normal male cell line .1.Sample C is a 1:1 mix of Sample A and Sample B. Sample C was included to dramatically increase (4x) the number of known variants with VAF between 1% and 2.5%4.Sample Spike-in  is Sample B with 5% AcroMetrix Spike hotspot synthetic controls Samples A, B, and C were prepared by Agilent and were kindly provided to SEQC2 for study. Sample Spike-in was prepared at Thermo Fisher Scientific  after receiving Sample B from Agilent under their material transfer agreement. All four samples were stored in low-EDTA TE buffer  at 20 ng/ul concentration.Eight oncopanel providers signed up to participate in this study, each provider recruited at least three independent laboratories which are remarkably familiar with their panels. A total of 28 testing laboratories were initially recruited. One laboratory was later excluded due to affiliation with the panel provider, one laboratory was excluded due to failing to pass quality control, and one laboratory was excluded due to an extended delay in experimental execution and data generation. We distributed the four reference samples to each laboratory. Four DNA libraries for each reference sample were then made at each laboratory as technical replicates. A total of 430 DNA libraries were prepared. Detailed information of the eight participating oncopanels are listed in Table 1, which was approved by the panel providers accordingly.Basic information about the experimental procedures and wet-lab QC metrics are summarized in Table Genomic DNA libraries were constructed for the reference samples according to the Agilent SureSelectXT HS Target Enrichment System for Illumina Paired-End Multiplexed Sequencing Library Protocol . In brief, 30 ng of each sample’s high molecular weight genomic DNA was sonicated in a 50 μl total volume with a Covaris E220 instrument to a mean size of 350 bp . DNA fragments were then end-repaired and A-tailed using a two-step cycling protocol (20 °C for 15 minutes and 72 °C for 15 minutes), followed by ligation to XTHS adaptors with UMIs for 30 minutes at 20 °C. Adapter-ligated fragments were amplified and indexed by PCR in a 50 μl total volume with Herculase II Fusion DNA Polymerase under the following conditions: 2 min at 98 °C , 10 cycle amplification of 30 seconds at 98 °C, 30 seconds at 60 °C, 1 minute at 72 °C, and 5 minutes at 72 °C . Library quality control (quantity and size distribution) was then assessed using either the 2100 Bioanalyzer and DNA 1000 assay or the 2200 TapeStation and D1000 screen tape. 1 μg of prepared gDNA libraries were then hybridized to a custom Immuno-Oncology focused Comprehensive Cancer Panel (1058 targets coding regions including UTRs and 7.6 Mb in size) biotinylated RNA probes  and captured with Dynabeads MyOne Streptavidin T1 beads. SureSelect enriched gDNA libraries were PCR amplified using on-bead protocol in a 50 μl total volume with Herculase II Fusion DNA Polymerase under the following conditions: 2 min at 98 °C , 10 cycles of 30 seconds at 98 °C, 30 seconds at 60 °C, 1 minute at 72 °C (amplification), and 5 minutes at 72 °C , followed by 4 °C hold. All DNA purifications between steps were performed with AMPure XP beads as indicated in the user manual. Post-capture library quality control was assessed using either the 2100 Bioanalyzer and a High Sensitivity DNA assay or the 2200 TapeStation and HSD1000 screen tape. Indexed samples were finally pooled and sequenced to approximately 5000X  or 10,000X (Sample C) read depth on a NovaSeq 6000 instrument using a 2x150bp Paired-End protocol (Q30 scores ≥ 75%).TM panel and further enriched following manufacturer instruction. The OncoScreenPlusTM panel is about 1.7 M bp in size and covers 520 human cancer related genes. Final DNA libraries were quantified using Qubit Fluorometer with dsDNA HS assay kit . A LabChip GX Touch System, Agilent 2100 bioanalyzer or Agilent 4200 TapeStation D1000 ScreenTape was then performed to assess the quality and size distribution of the library. The libraries were sequenced on the NovaSeq 6000 sequencer  with 2 × 150 bp paired-end reads with a unique dual index.The library prep and enrichment process were performed using Burning Rock HS library preparation kit without modification. In brief, DNA shearing was performed on SEQC2 gDNA reference samples using Covaris M220 for 195 S, with Peak Incident Power = 50 W, Duty Factor: 20%, Cycle Per Burst: 200, at 6–8 °C, followed by end repair, adaptor ligation and PCR enrichment. About 1 μg of purified pre-enrichment library was hybridized to the OncoScreenPlusLibraries were constructed using 100 ng from each of the reference samples in quadruplicate. Briefly, genomic DNA was sheared to 300 bp (Covaris) followed by library construction using the NEBNext Ultra II DNA Library Prep Kit for Illumina and xGen Dual Index UMI Adapters–Tech Access. End repair and A-tailing were performed according to the manufacturer’s recommendations. Adapters were ligated using 5 µL of 15 µM stock for each reaction followed by 0.9X AMPure purification. Libraries were amplified with Illumina P5 and P7 primers using NEBNext Ultra II Q5 Master Mix using 6 cycles of amplification. Libraries were purified using 1X AMPure clean up and quantified by Qubit. Following library construction, 500 ng of each library was captured with the xGen Pan-Cancer Panel v1.5 following the manufacturer’s instructions using xGen Universal Blockers–TS Mix. Hydrophilic Streptavidin Magnetic Beads and NEBNext Ultra II Q5 Master Mix were substituted for the capture and post-capture amplification using 16 cycles of PCR. Libraries were purified, quantified, and pooled for sequencing on an Illumina NovaSeq S4 flow cell.The iGeneTech AIOnco-seq gene panel was designed to target the exons of 113 cancer related genes. The total size of targeted regions was 944,153 bp. 100 ng of human genomic DNA was sheared on a Covaris S220 focused-ultrasonicator for 180 s . The resulting fragment-size distribution was about 150~250 bp. End repair, nontemplated dA-tailing, and adapter ligation were performed by using KAPA Hyper Prep Kits. The adapter was generated by annealing oligonucleotide ACACTCTTTCCCTACACGACGCTCTTCCGATC*T (the * represent a phosphorothioate bond) and oligonucleotide Pho-GATCGGAAGAGCACACGTCTGAACTCCAGTCAC. The ligation product was cleaned up and size-selected by using Beckman Ampure XP Beads (Beckman). The purified ligated product was amplified with 7 cycles of PCR to generate about 1 μg library.A mix containing 750 ng whole genome library, 2.5 μg human Cot-1 DNA (Invitrogen), 2.5 μg salmon sperm DNA, and 2000 pmol adapter blocking oligonucleotides was concentrated to 9 μl, and then heated at 95 °C for 5 min, and held at 65 °C for 5 min. A 13 μl of 65 °C prewarmed hybridization buffer (iGeneTech) was added to the library. A 7 μl of freshly prepared, 65 °C prewarmed mixture containing 200 ng biotinylated RNA probes and 20 U SUPERase-In (Invitrogen) was added to the library and mixed by a pipette. After 16 h at 65 °C, the hybridization mix was added to 50 ul Dynabeads MyOne Streptavidin T1 (Invitrogen), which had been washed three times and resuspended in 200 μl binding buffer (iGeneTech). The binding reaction was rotated on a rotational mixer (10 rpm/min) at 25 °C for 30 min, washed once at 25 °C for 15 min with 200 μl Wash Buffer I (iGeneTech), and wash three times at 65 °C for 15 min with Wash Buffer II (iGeneTech). The beads were resuspended in 20 μl TE Buffer. The post-hybridization PCR was performed to enrich and amplify the target region library. The libraries were sequenced on an Illumina HiSeq 2500 sequencer with 125 bp paired-end sequencing reads.5, briefly samples were sheared using Covaris to approximately 90–250 bp. Following shearing, DNA fragments were end-repaired and A-tailed in a single reaction, followed by ligation to a universal adapter. Post-ligation clean-up was performed using SPRI beads and then libraries were indexed using unique dual indexes by PCR. Target regions were captured using an overnight hybridization to biotinylated target specific oligos which covered 533Kb of genomic targets across 154 genes, followed by capture with streptavidin magnetic beads. A second hybridization and capture reaction were performed followed by PCR amplification using the universal primers compatible with Illumina’s sequencing flowcells. Libraries were normalized using bead-based normalization before being pooled in equal parts and sequenced, 10 samples per flowcell, on NextSeq v2 high-output flowcell. Sequencing reads were 2 × 101 bp with 8 bp dual indexed reads. Five independent labs were recruited as the test labs for ILM panel. One test lab was excluded from performance analysis due to an extensive delay in data generation and some deviation from the experimental protocol.DNA was processed according to the TruSight Tumor 170 Reference GuideThe Human Comprehensive Cancer QIASeq DNA Panel is a targeted enrichment panel that enriches genes using single primer extension and constructs libraries using integrated unique molecular indices (UMIs). The panel uses 11,311 primers in a single tube mix to enrich over 0.83 Mb region across full coding areas of 275 cancer genes. The selected genes harbor mutations that are commonly involved in cancer development and progression. Genomic DNA samples were first fragmented, end repaired and A-tailed within a single, controlled multi-enzyme reaction. The prepared DNA fragments were then ligated at their 5′ ends with an Illumina sequencing platform-specific adapter containing a 12-base fully random sequence of UMI and sample index. Therefore, each original DNA molecule in the sample received a unique UMI sequence during ligation prior to target enrichment and library amplification PCR. This allowed the correction of errors associated with PCR amplification and sequencing.Target enrichment with Human Comprehensive Cancer QIASeq DNA Panel was performed post-UMI assignment to ensure that targeting regions of DNA molecules containing UMIs were sufficiently and uniformly enriched in the sequenced library. For enrichment, ligated DNA molecules were subject to six cycles of targeted PCR using one region-specific primer and one universal primer complementary to the adapter. After enrichment PCR, nineteen cycles of Universal PCR were carried out to further amplify the target enriched library and added platform specific adapter sequences and additional sample indices. Finished libraries were checked on Bioanalyzer for proper size distribution and library concentration was measured by QIAseq Library Quantification system. Libraries were normalized to 5 nM and pooled together according to quantified library concentration. Pooled libraries were sent out to Illumina for sequencing on NovaSeq with a Qiagen custom read 1 sequencing primer. Libraries within a test site were loaded on each lane of the flowcell for sequencing.The panel design utilized in the study is 170907_HG38_SEQC2_PHC_EZ. This is a research panel, not intended for commercial development. The panel consists of coding sequence (CDS) from 33 cancer genes from SEQC Pan Cancer Gene list and CDS regions overlapping the Accugenomics controls. Additional targets were derived from the Roche Avenio ctDNA Panels. The panel consists of 45 genes in total (~131 kb of targets). The panel also targets 730 putative structural variant breakpoints derived from the 10X cell line sequencing (~206 kb of targets).The ROC panel utilized an enzymatic fragmentation library prep followed by a hybridization-based workflow. The extracted DNA sample (100 ng) was fragmented using the KAPA HyperPlus Library Preparation Kit, and then ligated to Illumina-compatible, unique-dual-index (UDI) sequencing library adapters (IDT). After the ligation, the DNA library was amplified using the KAPA HiFi HotStart Ready Mix. Amplified libraries were quantified with a fluorometric method, and the maximum available amount of library (> = 0.5 ug and < = 2.5 ug) was utilized for capture. The sample was incubated overnight at 47 C with the gene panel (170907_HG38_SEQC2_PHC_EZ), which consisted of biotinylated DNA probes designed to capture the genes and regions of interest. Universal Blocking Oligos were utilized in the hybridization to prevent cross-hybridization between sequencing adapters and to increase capture specificity. The desired DNA-probe complexes were then captured on streptavidin beads, and after a series of washes, first at 47 C and then at room temperature, the samples were amplified using ligation mediated PCR (LM-PCR). The final product of the workflow was enriched libraries ready for sequencing. The final sequencing libraries were sequenced (2 × 150 bp) using the Illumina NovaSeq sequencing platform.Four independent labs were recruited to test the ROC panel. Initial sequencing data QC analysis led to the exclusion of one test lab from performance analysis. Shorter fragments, which resulted from over fragmentation during library preparation, indicated a likely deviation from the experimental protocol.6 was used to generate libraries for next-generation sequencing on the Ion Torrent S5 platform. This assay enables analysis of variants across 146 genes and covers 350,350 bp of DNA target sequence. It typically is used in combination with an accompanying RNA assay bringing the gene total to 161, but the study used only the DNA assay with the RNA component omitted. The gene content of the Oncomine Comprehensive Assay was prioritized to detect relevant somatic variants in solid tumors with associations to published evidence including the indication statements of approved cancer drugs, consensus clinical treatment guidelines, and the enrolment criteria of oncology clinical trials. Purified DNA was extracted using the RecoverAll Multi-Sample RNA/DNA Isolation Workflow7. Four DNA samples were used to prepare duplicate libraries on each of two runs using the Ion Chef Instrument. An input of 10 ng per primer pool of DNA was used, and libraries were generated following the manufacturer’s instructions in the Oncomine Comprehensive Assay User Guide8 and the Ion AmpliSeq Library Preparation on the Ion Chef User Guide9. A total of 8 DNA sample libraries were combined for template preparation on the Ion Chef Instrument using the Ion 540 Kit-Chef10. Sequencing was performed with the Ion S5 XL System11 and the Ion 540 Chip12.The Oncomine Comprehensive Assay DNA v3C – Chef Ready Kit1.The libraries from the same panel were sequenced on the same sequencing platforms chosen by the panel providers. Even though four sequencing platforms were used in this study, including three Illumina platforms  and one ThermoFisher platform (IonTorrent S5), each library was sequenced on only one of the platforms. Comparison across sequencing platforms of the exact same DNA library is not available in this study. Sequencing data was required to be shared within the SEQC2 Oncopanel Working Group via Illumina BaseSpace Sequence Hub or by uploading data to the sFTP server hosted at Stanford University. Either FASTQ or BAM format was used for data sharing. All the data was collected at the National Center for Toxicological Research (NCTR), organized and renamed in a consistent manner, and then submitted to NCBI SRA data repository. The data became publicly available upon the publication of the related research manuscript1. If the inquisitive reader is interested in comparing the variant calling results between the recommended or in-house pipelines, the VCF files can be downloaded at figshare13.Each panel provider ran their recommended or in-house pipelines for variant calling and filtering. In this study, we considered each recommended or in-house pipeline to be part of the solution of variant detection along with the according oncopanel. The purpose of our study was not to determine the superior method for variant calling; thus, the recommended or in-house pipelines were used as they should perform reasonably at variant calling. The reproducibility, sensitivity, and false positive rate of the eight participating oncopanels were reported in the related research manuscript14. BioSample metadata are available in the NCBI BioSample database (http://www.ncbi.nlm.nih.gov/biosample/), where Sample A is under accession number SAMN1678636015 with download size of 879 Gb, Sample B is under accession number SAMN1678636116 with download size of 801 Gb, Sample C is under accession number SAMN1678636217 with download size of 1.37 Tb, and Sample Spike-in is under accession number SAMN1678636318 with download size of 2.47 Tb. There are 400 NCBI SRA records in total for this study, 103 for Sample A, 103 for Sample B, 91 for Sample C, and 103 for Sample Spike-in. Some of the data for Panel ILM was not available due to an unrecoverable data loss, thus, these data files are not submittable to NCBI SRA. A detailed list of all 400 NCBI SRA records can be found in Online-only Table 19, which contains 103 SRA records, can also be found under the BioProject PRJNA677997 umbrella. This sample was not used in the related publication1, but was designed for a side study to assess the usability of Accugenomics spike-in control as an orthogonal quality control in oncopanel sequencing. If interested in analyzing the Sample AIS, a brief description about the sample can be found in the Usage Notes. The “LibraryName” column shows the individual DNA library identifier, which combines the sample ID, panel code, testing laboratory code, DNA input amount, and library replicate together with “_”. There is a number “1” immediately after the panel code, which indicates the oncopanel study shown here. The “FileType” column show the file type of the according NCBI SRA record, where “fastq” indicates the FASTQ format and “bam” indicated the BAM format. Data for panel ROC is provided in unmapped BAM format. Data for TFS is provided in mapped BAM format, and the reference genome used for read mapping is listed in the “ReferenceGenome” column. Data for the other six panels is provided in FASTQ format. Details of read processing for all panels and read mapping for panel TFS can be found in Code Availability. Several filenames may be listed in columns “filename1” to “filename8”. These filenames are the original filenames used when uploading the data files to NCBI SRA. If retrieving the data using NCBI’s SRA ToolKit, one may see different filenames, which may be related to the SRR accession number. Filenames start with “LibraryName”, followed by part number if applicable, then the read identifier and filename extension. If the file type is “bam”, there is one file listed for each record, ending with “.bam” as the filename extension. If the file type is “fastq”, the files end with “.fastq.gz”. You will find pairs of files listed for each record, where “R1” and “R2” refer to the left and right reads. Specifically, for panel AGL, “R1” indicates the left reads, “R2” indicates the UMI barcodes, and “R3” indicates the right reads. To make the file size manageable for data transfer, we split bigger files into parts. For some records, there may be “PT1”, “PT2”, “PT3”, “PT4” in the filenames, which indicates different parts of the same data files. These parts of the same data files need to be merged in the order of 1 to 4 (or 2 if there are only two parts) before any data processing.The data have been deposited to NCBI SRA with accession number SRP295113Data from three testing laboratories  were excluded from the study due to the clinical lab being too closely affiliated with the panel provider (ST25), an extended delay in experimental execution and data generation (ST11), and over fragmentation of DNA samples during library preparation (ST17). Due to an unexpected hard drive failure, 20 files for panel ILM were unable to be recovered, including data for ST12 (LIB3 and LIB4), ST29 .1 (b) Sample Spike-in has known variants and shows very high sensitivity with a handful of readily explainable exceptions;1 (c) few false positive calls were reported with Sample B for all panels1, which indicates a low error rate of the sequencing data and high quality calling.The dataset is validated in multiple aspects, (a) each of the four reference samples was prepared and sequenced four times at one testing laboratory, and is also prepared and sequenced at least 12 times (three or more testing laboratories per oncopanel) for each oncopanel; high intra- and cross-lab reproducibility was observed;20. Due to the choice of reference genome in the oncopanel design, for all panels except ROC, the regions were provided for GRCh37/hg19; and for the ROC panel, the regions were provided for GRCh38/hg38. A blocklist of AcroMetrix Oncology Hotspot Control (AOHC) variants for panel TFS is also provided, which should be used when analyzing Sample AC5 with panel TFS.As all eight oncopanels were designed to target certain genomic regions, variant calling should be restricted to the designed regions of each oncopanel. The corresponding regions were provided in BED format and can be downloaded from figshare21.The extra Sample AIS was composed of Sample A and enzymatically fragmented Accugenomics spike-in control. As stated in our related work, the raw reads from Sample AIS should map against both the native template (NT) of reference genome (hg19 or hg38) and the modified internal standard (IS) genome template to separate the NT and IS reads4 were added to Sample B to make Sample AC5. These variants can be used as positive controls for analyzing Sample AC5. The list of Acrometrix Synthetic Hotspot controls is provided in VCF format for both hg19 and hg38 and can be downloaded from figshare22.The 500+ enzymatically fragmented Acrometrix Synthetic Hotspot controlsThis manuscript reflects the views of the authors and does not necessarily reflect those of the U.S. Food and Drug Administration. Any mention of commercial products is for clarification only and is not intended as approval, endorsement, or recommendation.


Quantitative understanding of uranium transport by high temperature fluids is crucial for confident assessment of its migration in a number of natural and artificially induced contexts, such as hydrothermal uranium ore deposits and nuclear waste stored in geological repositories. An additional recent and atypical context would be the seawater inundated fuel of the Fukushima Daiichi Nuclear Power Plant. Given its wide applicability, understanding uranium transport will be useful regardless of whether nuclear power finds increased or decreased adoption in the future. The amount of uranium that can be carried by geofluids is enhanced by the formation of complexes with inorganic ligands. Carbonate has long been touted as a critical transporting ligand for uranium in both ore deposit and waste repository contexts. However, this paradigm has only been supported by experiments conducted at ambient conditions. We have experimentally evaluated the ability of carbonate-bearing fluids to dissolve (and therefore transport) uranium at high temperature, and discovered that in fact, at temperatures above 100 °C, carbonate becomes almost completely irrelevant as a transporting ligand. This demands a re-evaluation of a number of hydrothermal uranium transport models, as carbonate can no longer be considered key to the formation of uranium ore deposits or as an enabler of uranium transport from nuclear waste repositories at elevated temperatures. Carbonate species may play a role in uranium transport in natural systems and nuclear waste repositories at high temperatures. Here uranyl carbonate species are shown to decrease in stability under hydrothermal conditions, suggesting that these species may not mediate transport after all. In most uranium ore deposits hydrothermal fluids leach uranium from moderately uranium-enriched source rocks , and concentrate it along geochemical boundaries that induce its precipitation from the fluid6. It is worth-noting that the same processes that enable its transport in ore deposits also present a hazard in waste repositories. While most repository designs aren’t expected to experience local rock temperatures above 100 °C9, those intended for dual-purpose canisters could potentially reach local rock and fluid temperatures in excess of 200 °C12. Similar transportation processes also need to be considered in nuclear accidents such as the Fukushima Daiichi incident, where hot fuel assemblies were submerged by seawater permitting uranium migration into the local environment14.Considering its low carbon footprint, high energy density and the emergence of novel technologies such as small modular reactors (SMRs), nuclear power is an attractive candidate for supplementing other low-carbon energy sources as the world continues to move away from fossil-fuel-based energy sources. Constructing and maintaining more reactors naturally brings with it the problem of producing enough fuel to keep them operational, and thus necessitates increased mining of and exploration for uranium resources (thorium technology notwithstanding). The majority of uranium ore deposits are typified by significant hydrothermal fluid activity occurring at temperatures ranging from 150 to 400 °C−, −, and 16. Thus, the presence of such ligands serves as a principal way to enhance the uranium carrying capacity of hydrothermal fluids. While naturally dependent upon ligand concentration, the predominance of a given complex is also strongly controlled by fluid pH. Under acidic conditions, uranyl complexes most readily with Cl− and 18, but sea water, many groundwater systems and a number  of uranium ore deposits are characterized by fluids with near-neutral/slightly alkaline pH ranges21.In general, uranium is most effectively transported by hydrothermal fluids when present in its hexavalent (U(VI)) oxidation state, which, as the uranyl ion (42−), and carbonate (CO32−) complexes16. It has been suggested that under such pH conditions and at elevated temperatures uranyl-carbonate complexes in particular could play an important role in uranium transport27. However, to date, uranyl carbonate complexation has only been experimentally explored at temperatures up to 70 °C28, with thermodynamic properties only being derived from room temperature experiments15. This means that all inferences and models made for elevated temperatures have to date been based on extrapolations of room temperature data. Recent high-temperature experiments on other uranyl complexes have shown that such extrapolations are seldom accurate, often being off by orders of magnitude29. This casts doubt on the accuracy and relevance of any high-temperature models that explicitly invoke carbonate as a potent transport enabler of uranium.At near-neutral/slightly alkaline pH and under ambient conditions, uranium mobility may be controlled by hydroxyl, biphosphate  were conducted to characterize the predominant uranyl complexes present in solution, and autoclave solubility experiments were performed to provide direct insights into the degree to which carbonate enhances the hydrothermal mobility of uranium. Overall, experiments were conducted over a temperature range spanning 100–250 °C—a range relevant to most uranium ore deposits and a few waste repository designs. Altogether, these experiments aimed to determine the stoichiometry and thermodynamic properties of the uranyl complexes responsible for uranium’s mobility in near-neutral, carbonate-bearing hydrothermal systems. The data collected demonstrate that the stability of uranyl carbonate complexes decreases dramatically with increasing temperature, suggesting that these species may not mediate hydrothermal transport of uranium after all.T) and pressure (P) condition. Raman experiments were performed on a solution containing 0.012 m UO3 and 0.1 m NaHCO3 (where m denotes moles of solute per kilogram of water). This carbonate concentration was chosen as an intermediate representative of the concentrations found in groundwater and uranium-bearing hydrothermal systems, which altogether typically span a range from 0.001 to 0.2 m, though higher concentrations have been suggested for some extreme systems23. Spectra were collected at 25, 50, 98, and 146 °C at water vapor pressure. Spectra were collected both immediately upon reaching the desired temperature and after 16–67 h to provide enough time for equilibrium to be reached. Although experiments at higher temperatures were planned, they were not performed due to the discovery of precipitation of uranium from solution, as will be discussed below. In planning these experiments it was assumed that, whereas the behavior of uranium in carbonate solutions is well characterized at ambient conditions and has been somewhat evaluated at T < 100 °C28, at temperatures above 100 °C its behavior in such systems was most in need of attention and experimental verification. At all temperatures, solubility calculations based on thermodynamic properties presented in the PSI Nagra Database30 as implemented by GEMS Selektor31 suggested that all uranium should have remained in solution, predominantly in the form of the uranyl tricarbonate complex, [UO2(CO3)3]4−. These predictions were confirmed by Raman spectroscopy at 25, 50, and 98 °C, with spectra corresponding to uranyl tricarbonate 2(s), though, if added to the model, Na2U2O7 was another possibility. Given that the wide body of prior work discussed above has relied on extrapolations similar to those used in our thermodynamic calculations, these results cast some doubt on the stated capability of carbonate-bearing solutions to carry appreciable concentrations of uranium at temperatures ≥150 °C.Our study commenced with a Raman spectroscopy investigation on carbonate-bearing solutions in which appreciable concentrations of uranium were dissolved at ambient conditions (further details may be found in “Methods” section). This technique permitted in situ observation of uranium’s bonding behavior with carbonate at each given temperature 34 (see Supplementary Methods for details). As with the Raman spectroscopy investigation discussed above, the HDAC XAS technique also permitted in situ solution characterization at temperatures and pressures of interest. This experiment was conducted on a solution containing 0.25 m NaHCO3 and 0.05 m UO3. EXAFS data Fourier transforms are reported in Fig. 3 feature on the spectra collected at T < 100 °C), become less prevalent when temperature exceeds 100–120 °C, leading to a decrease in the ability of carbonate-bearing solutions to transport uranium.To verify the discrepancy between the predicted2(OH)2(s)). This phase was determined to be stable at the investigated P-T conditions and solution compositions by both thermodynamic modeling (model details and data sources may be found in the “Methods” section) and post-experiment X-ray diffraction (XRD) measurements of the solids used. This technique takes advantage of being able to measure the change in U solubility as a function of ligand concentration—in this case carbonate (CO32−). For example, if uranium in solution were predominantly present as [UO2(CO3)3]4−, the primary equation describing its solubility would be UO2(OH)2(s) + 3CO32− ⇌ [UO2(CO3)3]4−] + 2OH−. Based on the associated equilibrium constant, the uranium concentration will increase by three orders of magnitude if carbonate concentration increases by one order of magnitude . By measuring this relationship, these experiments would determine if UO2(OH)2 solubility was in any way correlated to the presence of carbonate in high-T solutions, as well as permit the evaluation of the stoichiometry and thermodynamic molal properties of the predominant uranyl complex controlling solubility. Results from these experiments, collected at 200 and 250 °C and saturated water-vapor pressure, are summarized in Fig. 31. Numerical values can be found in the electronic supplementary data file 2, however given the solubility experiment results we deem this cause is unlikely or at least only a minor contributor to the loss of uranium from solution. It should be emphasized that the experimental conditions (both with regards to carbonate concentrations and temperature) investigated are indeed relevant to a number of natural and engineered  uranium-bearing hydrothermal systems.In stark contrast to theoretical predications, our data suggest that carbonate has no systematic effect on enhancing the solubility of uranium at elevated temperatures, and that the total solubility of uranium in carbonate-bearing solutions is significantly lower than expected—indeed, discrepancies of up to four orders of magnitude are observed between theory and our experiments. A potential explanation for the observed lack of solubility dependence on carbonate is the formation of a uranyl carbonate solid at elevated temperatures. We deem this unlikely as the separation of solution and solid during autoclave quenching . Indeed, the carbonate ion should only be considered as a relevant transport enhancer of uranium in natural waters at temperatures below 150 °C.−) complexes, and extrapolations suggest that this is also true at elevated temperatures16. However, such extrapolations are solely based on low temperature (25–85 °C) experimental data35, which, as already illustrated for carbonate, can lead to dubious predictions.A major question that arises in the context of our results is, if carbonate is ineffective at enhancing the mobility of uranium at temperatures above 100 °C, what complexes are instead responsible for uranium transport under such conditions? Furthermore, are these complexes more or less stable than theoretically predicted? At room temperature, the next most important group of complexes responsible for uranium transport in near-neutral fluids are the hydroxyl 2(s) + H+ ⇌ UO2OH+ + H2O]. Furthermore, discrepancies between theoretically calculated uranium solubilities range from about 1–3 orders of magnitude suggesting that the extrapolations of [UO2OH+] molal properties from 85 °C35 are qualitatively valid but require quantitative revision. Revised stability constants for UO2OH+ and comparisons with data available in the literature are reported in the Electronic Supplement. Hence, our results indicate that the true stabilities of uranyl hydroxyl complexes are significantly higher at elevated temperatures than currently believed.When plotted against pH, the uranium concentrations observed in our experiments show a dependence that suggests the predominance of the UO27 should be treated with skepticism. In addition, in such prior models, the invocation of carbonate as a transporting ligand was not only important for transport of uranium but deposition and accumulation as well. With the supposedly great capability of carbonate bearing fluids to transport uranium, any mechanism that leads to the loss of carbonate from such fluids, e.g., precipitation of solid carbonate phases, was often suggested as a means to induce uranium precipitation and thus lead to the development of economic uranium ore. In the absence of carbonate complexes, these schemes need to be abandoned and some other transport and depositional mechanisms need to be developed. We suggest that in such hydrothermal systems, under near-neutral to alkaline pH conditions, uranium is more likely to be transported as hydroxy complexes, chiefly [UO2OH]+.From the combination of Raman, XAS, and solubility results presented above, it is evident that carbonate complexes are irrelevant at temperatures above 100 °C in geofluids, and that the capability of carbonate to mobilize uranium under hydrothermal conditions has historically been greatly overestimated. This suggests that the invocation of carbonate as a means of transporting uranium in the near-neutral hydrothermal fluids that characterize a number of uranium deposits and transport systems −6 to 10−2.55 m (~240 ppb–670 ppm)38. Over the temperature and pH range investigated in our solubility experiments, we observed a maximum recorded uranium solubility of approximately 10−5 mol/kg, well within the range of dissolved uranium observed in natural systems. Furthermore, some uranium deposits are characterized by fluids whose pHs are controlled by mineral buffers such as Quartz-K-Feldspar-Muscovite and Kaolinite-Illite or the precipitation of hydroxyl-consuming phases39. Such buffers and processes tend to produce slightly acidic (pH 4–6.5) fluids, which according to calculations using our newly proposed thermodynamic properties could lead to the transport of significantly higher concentrations of uranium on the order of 10−3 m (Note how in the equation [UO2(OH)2(s) + H+ ⇌ UO2OH+ + H2O] an increase in the activity of H+, i.e., a decrease in pH, leads to more solubilised uranium). Such concentrations and pH conditions are typically associated with world-class uranium ore deposits such as those in the Athabasca and McArthur basins6. A notable implication of this is that the recent invocations of chloride and sulphate complexes17 as means to mobilise the significant concentrations of uranium required for world-class ore deposits may in fact be unnecessary. Instead, all uranium transport in such deposits could potentially be attributed to uranyl hydroxyl complex formation rather than the actions of other common inorganic ligands. This highlights an important avenue of further pursuit, namely, detailed characterization of the pH dependence of uranium aqueous solubility at elevated temperatures, which prior to this work has only been investigated up to 85 °C35, this would permit an accurate evaluation of the relative stabilities of uranyl chloride, sulphate, and hydroxy complexes over a range of geologically relevant pH conditions and ligand concentrations.Total dissolved uranium in ore-forming fluids varies over a wide range from around 1022, uranyl-carbonate complexation has often been cited as a primary concern for waste repository design largely due to its ubiquity and potency at room temperature conditions41. At thermal maximum, temperatures within the vicinity of stored high-level radioactive waste may span from 100 to 250 °C44 though naturally this is dependent on repository design. At such temperatures, uranyl-carbonate complexation is suppressed and our results additionally indicate that UO2OH+ becomes less stable with increasing temperature, leading to the rather counterintuitive conclusion that high temperatures may in fact stifle uranium liberation and transport from nuclear waste and that the risk of liberation increases as the waste cools and uranyl-carbonate complexes become relevant. Naturally we do not suggest that all repositories should be maintained at elevated temperatures but this conclusion suggests that, in terms of carbonate-bearing solutions, greater care should be considered to the later, cooler stages of a repository’s life when uranium transport by carbonate-bearing fluids becomes enhanced.The results of this work not only have implications for the study of uranium ore deposit formation, but they have environmental implications as well, specifically with regards to the design of nuclear waste repositories. As exemplified by the recent work of EwingTo conclude, results from the experiments reported here suggest that at temperatures above 150 °C the uranium carrying capacity of carbonate-bearing fluids typical of many uranium ore deposits and of concern for radioactive waste repositories may have been historically overstated. We have demonstrated that at such conditions carbonate is incapable of enhancing uranium mobility, which suggests that a significant number of uranium transport models may require revisiting and revision. On the other hand, we have also found that the carrying capacity of uranyl hydroxy complexes is significantly higher than historically believed, and that they could potentially account for a significant portion of the uranium carrying capacity of a wide range of geofluids.−1, a 1024 × 256 pixel CCD detector, an 1800 line/mm grating, and a confocal Olympus microprobe with an adjustable slit entry set to 200 µm. All spectra were obtained through a 20× objective lens  using light that was backscattered from the sample.Raman spectra were collected using a Horiba Jobin Yvon Evolution Raman spectrometer. The spectrometer is equipped with an 800 mm focal length, a polarized 532 nm, 250 mW Nd:YAG laser, an edge filter with a Stokes edge of 50 cm45 the samples were contained in fused silica capillary tubing , which were sealed by a hydrogen-oxygen torch. The capillary tubes were only half filled with liquid sample prior to sealing, and pressure was controlled through equilibrium with the gas phase . Temperature control was provided by a Linkam THMS600 stage (Linkam Scientific Instruments) coupled to a T96 temperature controller. In order to provide improved temperature control to the sample, the capillary tubing was housed in a custom-made aluminum heating block, manufactured to sit directly on top of the silver heating stage of the THMS600. This aluminum block is 16 mm in diameter, 20 mm in height, and has a 1 mm channel drilled through it to house the fused silica capillary tubing. Temperature calibration was performed using a K-type thermocouple cemented in a 1 × 2 mm capillary tube containing air.Expanding on the procedures first developed by Chou et al.3 in a 0.1 m NaHCO3 solution made using degassed de-ionized water. Separate samples of 0.012 m U + 0.1 m NaHCO3 were each heated at 50, 98, and 146 °C for 16–65 h, with Raman spectra taken at those temperatures, both pre-heating and post-heating. The inner surface was also investigated by a camera attachment to our Raman system to observe any possible precipitate.Samples were prepared by dissolving a measured amount of UOIII-edge (17166.3 eV) at beamline 11-2 at the Stanford Synchrotron Radiation Lightsource (SSRL). We performed these measurements up to 125 °C and 350 MPa using a hydrothermal diamond anvil cell (HDAC) with specially designed radioactive enclosure on a uranyl (UO22+) carbonate (CO32−) solution with [UO22+] = 0.05 m and [CO32−] = 0.25 m. The sample solution was prepared from a 2 mL solution of UO3·H2O dissolved in HClO4  by adding 1.2 mL of 0.5 m NaOH and 4.2 mL of 0.25 m NaHCO3. The measured sample consists of an aqueous liquid plus vapor bubbles that is placed into the HDAC sample chamber, defined by a 700 µm hole drilled at the center of a 125 µm (100 µm when compressed) thick rhenium (Re) gasket with an outer diameter of 3000 µm. Heating was achieved by resistive heaters near the diamonds, and temperature was measured with K-type thermocouples attached to each diamond. Pressure was achieved through application of force by tightening screws on the HDAC and, the pressure was estimated based on solution density. Further details about the experimental set-up are described by Dhakal et al.34.We performed X-ray absorption spectroscopy (XAS) at the uranium (U) L46.XAS data acquisition in fluorescence mode was made using a 100-element Canberra Ge solid-state monolith detector placed in the standard 90° orientation to the incident X-ray beam. The beam was focused to 250 μm in the horizontal direction and 1 mm in the vertical directions using Kirkpatrick-Baez mirrors, and the incident photon energy of the beam was varied using a double-crystal Si (220) monochromator. Detuning of the monochromator crystal was set at 15%. A yttrium (Y) foil in the beam path allowed energy calibration and calibration of XAS spectra to the Y K-edge . The 1st derivative with respect to energy of each XAS spectra was calibrated to this edge and data reduction including fitting and subtracting a background function using a pre-edge and post-edge function was performed using the Athena software package3, which was converted to the reference solid (UO2(OH)2) through exposure to experimental solutions and conditions as a reference solid. The experiments that investigated carbonate complexation (Series 1), were set up to have the reference solid interact with solutions containing variable quantities of carbonate added as NaHCO3. In the experiments investigating hydroxyl complexation (Series 2) these solutions instead contained a constant total carbonate concentration, which was added as varying ratios of NaHCO3 and Na2CO3 which was used to vary pH. Additionally, to ensure that our chosen activity model was applicable, all solutions also contained 1–2 m NaCl. Uranium is a redox sensitive element with its hexavalent and tetravalent oxidation states being the most common in natural systems. The speciation behaviors of both valence states are significantly different47, thus, to prevent interference and ensure that experiments were only characterizing the solubility and speciation of the hexavalent state a 1:1 Mn2O3/Mn3O4 mix oxygen fugacity buffer was introduced, which ensured that conditions inside each autoclave remained sufficiently oxidizing to stabilize U(VI) and prevented any U(IV) formation.Solubility experiments were designed to permit the evaluation of both the stoichiometry and formation constants of the predominant uranyl complex responsible for stabilizing uranium in high-temperature carbonate-bearing solutions. Experiments were conducted at 200 and 250 °C at saturated water-vapor pressure (SWP) using UO3  over a range of 0.001–0.4 m, with care being taken to ensure an ionic predominance of NaCl , whose concentration was kept at either 1 or 2 m. An ionic predominance of NaCl was required in order to permit the usage of the modified extended Debye-Hückel model50—activity model details are discussed further below. Based on recent high-temperature experimental data18 thermodynamic calculations suggested that such concentrations of NaCl result in negligible uranyl-chloride species formation at the pH conditions and temperatures investigated. A few solutions were made with higher NaHCO3 concentrations (up to 0.8 m) but their behavior differed little from those with lower concentrations—thus, to avoid any precipitation and minimize activity model issues, NaHCO3 concentrations were generally kept below 0.4 m. Based on the models for CO2 solubility in water and NaCl predominated fluids reported by Duan and Sun51, such concentrations of carbonate were stable in solution with outgassing of CO2 deemed unlikely. In Series 2 experiments the concentration of carbonate was kept constant at 0.3 m, where, based on Series 1 experiments, carbonate complexation was presumed negligible. This carbonate was added as varying ratios of NaHCO3 and Na2CO3  and was used to both control and buffer pH. Being able to vary pH in such a stable manner allowed us to investigate the relation between pH and uranium solubility, thus permitting the investigation of uranyl hydroxyl speciation. Solution compositions may be found in the Supplementary Material.In Series 1 experiments, carbonate concentration was varied using NaHCO2. Specific volumes of experimental solution were added such that the holders containing uranium were only submerged at the target experimental temperature via thermal volumetric expansion. The oxygen fugacity buffer holder was made tall enough such that it was never submerged. Loaded autoclaves were initially heated to 150 °C for 2 days to permit equilibration between the oxygen fugacity buffer, the atmosphere within the autoclave and the uranium solid—this ensured that subsequent solution–solid interactions solely involved hexavalent uranium. The experimental solution did not interact with the uranium solid during this pre-heating phase. After this preheating phase, solutions were heated to the target temperature and maintained at such for 5 days  to ensure complete equilibration between uranium solids and carbonate solutions. Autoclaves were then extracted and quenched in air to isolate solutions from solids. After opening, both holders were removed and concentrated nitric acid (MilliporeSigma AqueousOmniTrace) was added to the experimental solutions were then allowed to soak for 24 h, this was done to dissolve any uranium that may have precipitated during quenching. Solutions were then extracted from autoclaves and uranium concentrations were measured using Inductively Coupled Plasma Mass Spectrometry (ICP-MS).Experiments were conducted using Teflon-lined titanium  autoclaves into which carbonate-bearing solutions were placed alongside separate small Teflon holders containing the oxygen fugacity buffer and uranium reference solid. Perforated Teflon plugs were placed in all holders to prevent escape of any particulates, while still permitting interaction between solid reagents and solution/internal atmosphere. At all times, reagents were solely in contact with Teflon surfaces, thus precluding any unexpected chemical interactions with Ti/TiO2(OH)2) was stable under the chosen experimental conditions. To confirm this, samples of the reference solid were extracted after the experiments were quenched and were characterized using powder X-Ray Diffraction (PXRD). Quantitative phase analysis was performed using the Rietveld method52 and confirmed the sole presence of UO2(OH)2, primarily in its alpha form (~66%) with minor quantities  of its beta and gamma polymorphs. Measurement results may be found in the electronic supplement data file.To accurately determine formation constants a stable reference solid was required. Thermodynamic calculations suggested Paulscherrerite  2(cr), UO2Cl+, UO2Cl20 using data sourced from Shock et al.53, Sverjensky et al.54, Tagirov et al.55, Miron et al.56, and Migdisov et al.18. Data for H2O were sourced from the work of Marshall and Franck57.In all thermodynamic calculations, we defined the experimental system with the following aqueous species: HSupplementary InformationSupplementary Data 1Peer Review FileDescription of Additional Supplementary Files


ApoE ε4 compared to other populations. Ancestry analysis showed that the difference in risk between African and European populations lies in the ancestral genomic background surrounding the ApoE locus . Identifying the mechanism(s) of this protection could lead to greater insight into the etiology of Alzheimer disease and more personalized therapeutic intervention. Our objective is to follow up the local ancestry finding and identify the genetic variants that drive this risk difference and result in a lower risk for developing Alzheimer disease in African ancestry populations. We performed association analyses using a logistic regression model with the ApoE ε4 allele as an interaction term and adjusted for genome-wide ancestry, age, and sex. Discovery analysis included imputed SNP data of 1,850 Alzheimer disease and 4,331 cognitively intact African American individuals. We performed replication analyses on 63 whole genome sequenced Alzheimer disease and 648 cognitively intact Ibadan individuals. Additionally, we reproduced results using whole-genome sequencing of 273 Alzheimer disease and 275 cognitively intact admixed Puerto Rican individuals. A further comparison was done with SNP imputation from an additional 8,463 Alzheimer disease and 11,365 cognitively intact non-Hispanic White individuals. We identified a significant interaction between the ApoE ε4 allele and the SNP rs10423769_A allele,  in the discovery data set, and replicated this finding in Ibadan  and Puerto Rican  individuals. The non-Hispanic Whites analyses showed an interaction trending in the “protective” direction but failing to pass a 0.05 significance threshold . The presence of the rs10423769_A allele reduces the odds ratio for Alzheimer disease risk from 7.2 for ApoE ε4/ε4 carriers lacking the A allele to 2.1 for ApoE ε4/ε4 carriers with at least one A allele. This locus is located approximately 2 mB upstream of the ApoE locus, in a large cluster of pregnancy specific beta-1 glycoproteins on chromosome 19 and lies within a long noncoding RNA, ENSG00000282943.African descent populations have a lower Alzheimer disease risk from ApoE ε4 for developing Alzheimer disease. The mechanism of the interaction with ApoEε4 is not known but suggests a novel mechanism for reducing the risk for ε4 carriers opening the possibility for potential ancestry-specific therapeutic intervention.This study identified a new African-ancestry specific locus that reduces the risk effect of  ApoE ε4 and Alzheimer disease risk have been confirmed worldwide, but there is variability in the effect size across populations. African-descent populations have a lower risk from ApoE ε4 compared to other populations. Studies in admixed populations have shown that the African ancestral background surrounding the ApoE gene reduces the ε4 risk allele effect. Our objective in this study was to identify areas of the genome that interact with ApoE ε4 in African ancestry and result in a lower risk for developing Alzheimer disease. In this study we identify a protective locus for the ApoE ε4 allele that lowers the risk for African carriers of the ApoE ε4 allele to get Alzheimer disease from an odds ratio of 7.2 to 2.1. This protective haplotype has a frequency of 12% in the African ancestry, but only 0.003 in Europeans. This has been replicated in three independent African ancestry datasets and is trending in a much larger European dataset.Strong associations between  ApoE) gene (19q13.32) is the strongest genetic risk factor for late-onset Alzheimer disease (AD) and is associated with an earlier age-of-onset [ApoE ε4 allele increases AD risk, while the ε2 allele decreases AD risk (e.g. provides a protective effect) relative to the other two alleles [The apolipoprotein E (oE) gene 9q13.32 iApoE ε4 allele has a heterogeneous AD risk effect across diverse ancestral populations [ApoE ε4 for AD is in East-Asian populations, with the lowest risk from ApoE ε4 in African (AF)-Ancestry populations ) [ApoE ε4 allele contributing to this difference in population risk. Using admixed populations with the substantial proportion of AF ancestral genetic background  and the Dominican Republic), two independent studies [ApoE locus . Specifically, when the ApoE ε4 allele lies on an AF-originated haplotype the AD risk is significantly lower than if it lies on EU-originating haplotypes. Simply put, an individual who has inherited their ApoE ε4 allele from an AF ancestor has the lower ApoE ε4-associated AD risk observed in AF populations, while an individual who has inherited their ApoE ε4 allele from an EU ancestor has the AD risk observed in EU.Identifying protective variants against the development of AD has been a key goal of different research groups, including the AD Sequencing Project . The ideulations  Fig 1)..ApoE ε4 ns (AA)) ,6–10. Th studies ,12 demonApoE ε4 in African ancestry. We have assessed the ApoE ε4 haplotypes of both EU and AF local ancestry using several genomic approaches [ApoE ε4 carriers. This locus lies in the pregnancy specific beta-1 glycoproteins (PSG) gene cluster on chromosome 19, approximately two megabases (mB) upstream of the ApoE locus.Our objective is to follow up the local ancestry finding and identify the genetic variants that lower the risk for proaches ,14. We rPSG genes on chromosome 19 approximately 2 mB from the ApoE gene. This locus has a significant interaction with the ApoE ε4 allele  in the AA samples, meeting an FDR correction threshold for multiple testing . Individuals carrying the minor allele “A” at rs10423769 showed a reduction in AD risk due to ApoE ε4. ApoE region for the interaction term . The NHW analyses showed an interaction trending in the “protective” direction but failing to pass a 0.05 significance threshold   that lies within a large cluster of 26×10−2) . The maiApoEε4 risk allele for AD in individuals with the homozygous AF local ancestry within two subgroups stratified by the rs10423769_A allele. In one subgroup, we included individuals homozygous for the “G” allele and in the other we included individuals with at least one alternative “A” allele. To assess the AD risk effect of ApoE genotypes ε3/ε4 and ε4/ε4 relative to the ε3/ε3 genotype, we restricted the sample set to those that were not ε2 allele carriers. Only four PR individuals were identified with the rs10423769_A allele and homozygous AF local ancestry, so we did not include individuals from PR for further analysis. We performed logistic regression analysis within AA, Ibadan populations, and in combined AA and Ibadan individuals separately. Then, we tested the risk effect size differences of ε3/ε4 and ε4/ε4 genotypes using two-sample z-test. We found that in the subgroup of rs10423769_A allele carriers the effect sizes of ε3/ε4 and ε4/ε4 genotypes were significantly lower than in the non-carriers . Odds Ratios for developing AD according to ApoE genotypes stratified by the rs10423769_A allele in AA and Ibadan populations are shown in Next, we investigated the modifier effect of the PSG2 gene. This haplotype lies within the long noncoding gene ENSG00000282943 , primarily expressed in the cerebellum and fibroblasts based on data from GTEx [The AF haplotype associated with the “A” allele for rs10423769 is shown in rom GTEx .TMEM145 gene in the cerebellum with the “A” allele having a 1.6-fold increase in splicing levels between chr19:42320437 and chr19:42320653 relative to the “G” allele .Splicing quantitative trait loci (sQTL) analysis using the GTEx database shows that rs10423769 is a significant sQTL for the ApoE gene locus via cis 3D chromatin looping. As ApoE is primarily expressed in astrocytes and microglia, we performed Hi-C analysis in iPSC-derived astrocytes from ApoE ε4 homozygotes who were homozygous for either African or European local ancestry surrounding ApoE ε4. As shown in PSG2 and ApoE reside in separate topologically associated domains (TADs) and no cis enhancer-promoter loop was detected between the two loci in either local ancestry. However, cerebellum data were not available.We used Hi-C analysis to investigate if the locus represented by rs10423769 directly interacts with ApoE ε4 homozygotes in AF ancestry by approximately 75%. Previous studies have shown that the African local ancestral background of the ApoE gene reduces the AD risk due to the ε4 allele, with individuals inheriting the ApoE ε4 allele from African ancestors having a lower risk of AD than individuals inheriting the ApoE ε4 allele from European ancestry [) that explains a portion of the lower risk due to ApoE ε4 in African local ancestry individuals. A recent single-nuclei RNA study showed that European local ancestry carriers had significantly higher ApoE expression than African local ancestry carriers, suggesting ancestral-specific regulation of ApoE gene expression [ε4 allele risk effect among populations.This study identified a new AF ancestry-specific haplotype that reduces the AD risk effect of ancestry ,12. Our pression  could beApoE gene to functionally include the topological associated domains surrounding ApoE. The current study expands the Rajabli et al study to a wider genetic region that includes +/-3 mB around the ApoE gene, but still correlates with the previously identified effect of the local ancestry associated with differences in risk for AD between ApoE ε4 carriers.Local ancestry blocks across the genome have a wide distribution in size. In our study’s largest dataset of AA individuals (3000) the mean of local ancestry block sizes across the genome was ~36Mb . The locPSG2 and ApoE in individuals with African ancestry, we asked if the two loci have cis interaction with each other that could explain the statistical finding. Towards this end, we constructed a chromatin 3D interaction map in iPSC-derived astrocytes with African local ancestry and European local ancestry surrounding ApoE using Hi-C. No evidence of cis interaction was observed in this cell type, which along with microglia, is the major cell that expresses ApoE. As most enhancers interact within 1 mB [ApoE is (~2 mB) but can’t be completely ruled out.Since we detected a statistical interaction between hin 1 mB , it is uTMEM145, which has not been implicated in AD, although it has been reported to be upregulated in anterior cingulate cortex in Dementia with Lewy body patients [TMEM145. The cerebellum has had observed changes in AD, especially in early onset forms of the disease. But it has received little investigation in the pathophysiology of AD, as historically the cerebellum has been studied for its role in the regulation of motor activity. However, recent studies have shown a role for the cerebellum in cognition, including roles in working memory and executive and visuospatial functions [Indeed, the distance between the two interacting loci suggests that other mechanisms than enhancer-promoter maybe involved in this protective effect. The protective haplotype overlies the long noncoding RNA ENSG00000282943, but little is known about its specific function. Interestingly, rs10423769 is reported to be a sQTL for patients . Interesunctions –20.PSG genes, a family of glycoproteins that are primarily synthesized in the syncytiotrophoblast of the human placenta. Several case-control studies have shown that low levels of PSG2 are associated with pre-eclampsia [The protective haplotype also lies within a large cluster of clampsia , which, clampsia . Indeed,clampsia . As thesApoE ε4 is the major genetic risk factor, this work further supports growing efforts to explore it as a major therapeutic target for AD.This finding is the first AF specific protective effect in AD and highlights the importance of diversity and the inclusion of all populations into research. It hopefully will encourage additional studies focused on diverse populations, where allelic frequency differences can discover information that is hidden when studying only a single population. With most clinical trials in AD ending in failure, the identification of natural protective interactions is of key importance in moving therapeutic efforts in AD forward for all ancestries. Finally, as Written consent was obtained from all participants and study protocols were approved by the University of Miami Institutional Review Board (IRB), the Indiana University IRB and the IRB of the University of Pennsylvania.Our study consisted of a discovery phase using TOPMed imputed genotype data from AA individuals in nine datasets  and a reApoE genotyping was performed in the individual datasets as summarized in the Genome-wide single-nucleotide polymorphism (SNP) genotyping was processed on multiple different platforms and Samples from Ibadan and PR had WGS performed at the Uniformed Services University of the Health Sciences (USUHS) using standard protocols as previously described . IlluminStandard quality control (QC) for genotype and individual-level data were performed for each dataset using software PLINK v.2 . Variant2 > 0.8) with a minor allele frequency (MAF) > 0.05 in the existing AA datasets.We imputed AA and NHW genotype array datasets individually using the TOPMed R2 version panel (build of GRCh38) and TOPMed Imputation server . The TOPWe calculated global ancestry ) within each array dataset using the EIGENSTRAT approach using EIGENSOFT software with no reference population . To estiApoE ε4 risk effect, we performed an interaction analysis in our discovery AA datasets, focusing on the broad genetic region that includes +/-3 mB around the ApoE gene. Imputed data were force-called to the most likely genotype (with 0.90 threshold for the probability) and then assessed using a logistic regression approach. Our primary model for the logistic regression included AD status as the outcome (dependent) variable. Independent variables included force-called genotype and ApoE ε4 main effects along with an interaction term between genotype and ApoE ε4. Additional covariates included age, sex, and genome-wide ancestry (PC1:3)  and performed interaction analysis in each imputed AA datasets separately. Then, we meta-analyzed all terms across AA datasets by applying fixed-effect meta-analysis (assuming similar effect sizes) from METASOFT software [To identify protective loci that modify the  (PC1:3) . We codesoftware . We usedsoftware . SubsequApoE ε4 risk effect, we compared the AD risk effect of ApoE ε3/ε4 and ε4/ε4 genotypes relative to the ε3/ε3 genotype in those that were carriers of the modifier alleles to those that were not carriers. First, we restricted the sample set to those that were homozygous for the AF genetic ancestry around the ApoE locus and were not ApoE ε2 allele carriers. We then stratified by carrier status at loci identified as FDR significant. Next, we performed logistic regression with ApoE genotypes (ε3/ε3 (reference), ε3/ε4, ε4/ε4), age, sex, principal components (PC1-3), and batch as covariates within each group  for each study and across studies. Finally, we tested the difference in effect sizes of the ApoE genotypes between carriers and non-carriers of the putative modifiers using two-sample z-test. Statistical analyses were performed using the “GLM2” package available in R computing environment [To assess the influence of putative modifiers on ironment .ApoE ε4/ε4 and had AF ancestry. Cells were differentiated and cultured using the StemDiff Astrocyte Differentiation and Maturation kits (StemCell Technologies) according to the manufacturer’s protocol. In situ Hi-C libraries were prepared using the protocol adapted from Rao et al. [Hi-C analysis was performed on astrocytes derived from induced pluripotent stem cells (iPSCs) derived from AD patients who were o et al. . For eaco et al. . Duplicao et al. .S1 Appendix(DOCX)Click here for additional data file.S1 Methods(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig10 scale.Admixture block sizes are displayed along the x-axis in log(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.


Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/fβ noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/fβ). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/fβ noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG.The dynamical and physiological basis of alpha band activity and 1/ fβ scaling of the power spectrum. While these phenomena are well studied a thorough understanding of their respective generative processes remains elusive. By employing a theoretical approach that follows from neural population models of EEG we demonstrate that it is possible to economically account for both of these phenomena using a singular mechanistic framework: resting EEG is assumed to arise from the summed activity of multiple uncorrelated, stochastically driven, damped alpha band linear oscillatory processes having a distribution of relaxation rates or dampings. By numerically estimating these damping distributions from eyes-closed and eyes-open EEG data, in a total of 136 participants, it is found that such damping distributions are predominantly bimodal in shape. The most weakly damped mode is found to account for alpha band power, with alpha blocking being driven by an increase in the damping of this weakly damped mode, whereas the second, and more heavily damped mode, is able to explain 1/fβ scaling present in the resting state EEG spectra.The resting human electroencephalogram (EEG) exhibits two dominant spectral features: the alpha rhythm (8–13 Hz) and its associated attenuation between eyes-closed and eyes-open resting state , and the 1/ EEG timFirst discovered and recorded in the early 20th century by Hans Berger , the alpThere exists significant heterogeneity of alpha band activity across the population in the terms of its the magnitude of its activity, its peak/central frequency and its reactivity to changes in behavioural and physiological state . TopograAlpha blocking is most obvious in the power spectral density of the recorded resting EEG, where a significant attenuation of peak alpha power is observed in the transition from EC to EO states. Such changes in power are also observed across a range of cognitive tasks , 14. Thefβ (β ≈ 1 − 2) scaling of the power spectral density  were generated. The damping distributions for a given regularisation parameter that resulted in the minimum RSS error was deemed the best solutions. The regularisation scheme performed well for a unimodal distributions .By using the empirically estimated damping distributions in the forward problem i.e  to generβ, were computed across all subjects and channels for both the experimental spectra and the model by fitting a 1/fβ profile over the frequency range 15–40 Hz. Pooled data  analysis revealed a range of experimental spectral scaling across EC and EO conditions  and EO conditions   . In geneTo quantify alpha blocking we used resting state data recorded in EC and EO conditions from two separate EEG data sets having a combined total of 136 participants, and computed channel based power spectral densities using Welch’s method. A total of 8677 (109 subjects × 64 channels + 27 subjects × 63 channels) power spectra were estimated for each recording condition. The Jensen-Shannon divergence was then computed between each EC/EO spectral pair to measure the degree of alpha blocking across the respective data sets. A range of alpha blocking was observed across the both data sets; some participants evinced little blocking whereas others exhibited strong alpha band attenuation between states. Example cases are plotted in WDMEC − WDMEO), on an electrode by electrode basis for both data sets. Group level averages were computed and plotted topographically such that direct comparisons can be made between the topography of alpha blocking quantified using the Jensen-Shannon Divergence  and data set 2  On this basis we can reasonably conclude that alpha blocking is being driven by increases in the damping of a weakly damped population of alpha band oscillatory processes.As expected group-level weakly damped measure difference values were largest in the occipital regions for both data sets . HoweverOur aim was to explore whether the resting EEG could be accounted for in terms of a sum of damped alpha oscillatory processes and in particular whether changes in the power spectrum between EC and EO states could be reasonably accounted for solely by changes in the damping of such oscillatory processes. Through the use of regularisation methods, we estimated EC and EO damping distributions across a total of 136 participants, available from two independently collected EEG data sets, and characterised their features. We found the model performed well when applied to real EEG spectra, providing high quality fits to both the alpha band and the high frequency power-law-like scaling. The attenuation of the peak alpha power observed in heavy alpha blockers seems to be well explained by an increase in damping between EC and EO states as we found that the distribution of damping became more heavily damped in EO states than in EC when alpha blocking was present. Our results therefore suggest an economical approach to describing the resting EEG spectrum in terms of the summed activity of stochastically driven alpha band damped linear oscillations and represents an alternative way to characterising the spectral properties of resting EEG.f’ noise in resting EEG, in contrast to the dominant view, might be accounted in terms of a single underlying mechanism. This is significant as the mechanistic genesis for the alpha rhythm and its EC-EO induced changes remains uncertain. The received view for the origin of the alpha rhythm is that it arises principally via reverberant thalmo-cortical neuronal population interactions , however the current parameterisation is ignorant to this). From We begin by noting that most linearised mean-field models generally account for the EEG signal, response , 91 for Ge(ω)|2 can be calculated as:fα, ge(t) and our choice is merely the simplest, we choose the numerically simpler reduction of By assuming the electrocortical impulse response of fα is assumed constant and remains physiologically fixed, while the damping γ varies:We now assume that the signal measured at a single location is the sum of many such processes arising from multiple neural populations, where the centre alpha frequency, S(f), is then,The estimated power spectral density for this model process, Ni(f) are all uncorrelated such that their cross-spectrum vanishes,the white noise driving processes Ni(f) are broadband and of equal power (RMS) i.e. Ni(f) → N (i.e. is a constant),the We now assumereducing our estimated model power spectral density toM, is sufficiently large that we can define a distribution of dampings, p(γ), such that the number of oscillators with damping between γ and γ + dγ, is Mp(γ)dγ, where ∫p(γ)dγ = 1. On this basis c = kNM and Se ≡ |Ge|2. For our purposes S(f) will be empirically estimated directly from EEG data and Se is our theoretically specified spectral response (γh) and lower (γl) limits of the damping distribution are chosen such that a broad range of spectral behaviour is covered i.e. both large and sharp alpha peaks and those which are heavily damped.We now make one final assumption that the total number of damped linear oscillators, response . Substitrst kind . In pracFredholm integral equations of the first kind are inherently ill-posed and difficult to solve . For thic into ki(γ) = k, we then discretize the integralOur general Fredholm integral equation of the first kind is thenS is an N × 1 data vector (power spectrum), K is an N × M coefficient matrix with entries consisting of the kernel evaluated at each damping value and P is the unknown distribution vector M × 1 in dimensions. Simply inverting the equationS and those caused by the numerical inversion. By applying appropriate regularization to P that minimizes the following function forI is the Tikhonov Matrix, which is generally chosen to be identity matrix, I, multiplied by a regularization parameter, λ and ‖⋅‖2 is the Euclidean (or L2) norm. In practice, we solve −5 ≤ λ ≤ 100 divided into 100 logarithmically spaced steps, with the optimal regularization parameter chosen such that it finds the maximum entropy solution that falls within an error bound of approximately 2.5% of the minimum residual sum of squares error (RRS) between the generated power spectrum and the data power spectrum. The maximum entropy distribution within that error bound was chosen as it favours distributions that are smoother and broader. See Finally, we can arrange the above summation as a set of linear equations in matrix notationquestion . For therp the ratio between the peak magnitudes, μγ the mean and σγ the standard deviation. For bi/trimodal structures, the damping distributions were produced by summing multiple Gaussians. The relevant model parameters tested are presented below:Unimodal: Damping interval [0 ≤ γ ≤ 10] s−1, μp = 5 s−1, σ = 5 s−1.Bimodal: Damping interval [0 ≤ γ ≤ 50] s−1, μ1 = 5 s−1, μ2 = 25 s−1, standard deviation σ = 5 s−1 and rp =  and .Trimodal: Damping interval [0 ≤ γ ≤ 50] s−1, μ1 = 5 s−1, μ2 = 20s−1, μ3 = 40 s−1, σ = 5 s−1 and rp = , , .A simulation study was conducted to validate the Tikhonov regularisation method. Damping distributions of known form that were uni/bi/tri-modal with differing relative mode areas were used in the forward problem to generate simulated power spectra. The simulated power spectra were then used as the input to the regularised inversion method of the previous section to recover the known distributions. The simulated damping distributions were Gaussian in form−6 ≤ λ ≤ 1] was used and divided into 100 logarithmically spaced steps.A successful recovery of a known distribution was measured using a RSS cost function between the known distribution and the recovered distribution. A regularisation parameter range of  within the alpha band using MATLAB curvefit toolbox functions. Because a comparison between the experimental and model spectral slope for frequencies is explored, a spectral scaling parameter β was calculated by fitting a 1/fβ profile to the power spectrum over a frequency interval from 15–40 Hz. This procedure was performed on all the experimental spectra and their respective model spectra.The peak alpha frequency, DJS, between the respective EC and EO power spectra is used to quantify the level of alpha blocking. The JSD is a symmetric and non-negative measure of the distance between two probability distributions. By normalizing the power spectra to unity, we can treat each as a probability distribution allowing us to compute the distance between the EC and EO spectra in probability space. The JSD provides a useful way to measure the degree of alpha blocking between resting states. The expectation is that the larger the attenuation of alpha band activity between EC and EO states, the larger the JSD will be between the two distributions. The JSD is based upon the Kullback-Liebler divergence  for EO and EC conditions. Given the differing channel layouts of the respective data sets separate group level topographic plots were calculated with any summary statistics being computed separately across the data sets.Hi0,…, is assumed in which there is no significant difference between EC and EO resting states. Given that multiple hypothesis tests are being conducted in parallel, the permutation test was corrected for the multiple comparison problem. Here we use the Max-t/Min-t method detailed in forPermutations: 1,…,Zdo2 forColumns: 1,…,Mdo3    Collate the EC and EO column values into a joint vector and randomly shuffle the entries (this shuffle is between conditions for each channel).end  forColumns: 1,…,Mdo4    Split the shuffled joint vector back into EC/EO condition vectors. Compute the test statistic of interest for each shuffled joint condition column vector.end  forColumns: 1,…,Mdo5    Calculate the maximum (Max-t) and minimum (Min-t) value for each test statistic across all channels and record it.end  end6 Plot the Max-t and Min-t values to obtain the null hypothesis distribution.S1 Figc in Empirical demonstration of the equivalence of the constant (PDF)Click here for additional data file.S1 AppendixFurther details regarding the selection of the optimal regularisation parameter as well as an illustration of the systematic dependency of model fit on the regularisation parameter chosen.(PDF)Click here for additional data file.S2 AppendixGraphical illustration of the properties of the WDM defined in (PDF)Click here for additional data file.