Whole-genome Sequencing Datasets Research Articles

Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.

Phylogenomic analyses revealed widely occurring hybridization events across Elsholtzieae (Lamiaceae)

Obtaining a robust phylogeny proves challenging due to the intricate evolutionary history of species, where processes such as hybridization and incomplete lineage sorting can introduce conflicting signals, thereby complicating phylogenetic inference. In this study, we conducted comprehensive sampling of Elsholtzieae, with a particular focus on its largest genus, Elsholtzia. We utilized 503 nuclear loci and complete plastome sequences obtained from 99 whole-genome sequencing datasets to elucidate the interspecific relationships within the Elsholtzieae. Additionally, we explored various sources of conflicts between gene trees and species trees. Fully supported backbone phylogenies were recovered, and the monophyly of Elsholtzia and Keiskea was not supported. Significant gene tree heterogeneity was observed at numerous nodes, particularly regarding the placement of Vuhuangia and the E. densa clade. Further investigations into potential causes of this discordance revealed that incomplete lineage sorting (ILS), coupled with hybridization events, has given rise to substantial gene tree discordance. Several species, represented by multiple samples, exhibited a closer association with geographical distribution rather than following a strictly monophyletic pattern in plastid trees, suggesting chloroplast capture within Elsholtzieae and providing evidence of hybridization. In conclusion, this study provides phylogenomic insights to untangle taxonomic problems in the tribe Elsholtzieae, especially the genus Elsholtzia.

PGPg_finder: A comprehensive and user-friendly pipeline for identifying plant growth-promoting genes in genomic and metagenomic data

Identifying and comparing plant growth-promoting traits (PGPT) within whole-genome and metagenomic sequencing data can significantly advance agricultural research and promote sustainable crop production. This study introduces PGPg_finder, a comprehensive pipeline designed to annotate and compare PGPT from both whole-genome and metagenome sequencing datasets. This pipeline utilizes direct sequence annotation alongside de novo assembly methods to accurately detect PGPT. By cross-referencing sequences from the PLaBAse database, it identifies and quantifies the presence of these genes within the original datasets, facilitating an intuitive comparison of the abundance and distribution of PGPT across various samples. We evaluated the performance of PGPg_finder by analyzing genomes from five rhizobacterial strains: Paenibacillus vini, Paenibacillus polymyxa, Fictibacillus sp., Brevibacillus agri, and Bacillus cereus, and also metagenomic samples from bulk soils subjected to forest-to-pasture conversion in the Amazon rainforest. The genomic workflow revealed several genes associated with substrate utilization, abiotic stress neutralization, phosphate solubilization, and iron acquisition. It also identified genes unique to specific lineages, including those associated with colonization and plant-derived substrate usage in P. polymyxa, quorum sensing response and biofilm formation in P. vini, heavy metal detoxification and nitrogen acquisition in B. agri, and spore production and neutralizing biotic stress in B. cereus. The strain Fictibacillus sp. presented several unique genes related to surface attachment, stress response, xenobiotic degradation, phosphate solubilization, and phytohormone production. The use of PGPg_finder highlights its potential to uncover novel inoculants and strains. The metagenomic workflow distinguished plant-growth promotion gene profiles between soils from the Amazon rainforest and pasture, with the latter showing a profile more aligned with simple carbohydrate consumption, abiotic stress tolerance, motility and chemotaxis, and phosphorus mineralization. Native forests exhibited a profile associated with the degradation of complex organic matter, oxidative stress tolerance, xenobiotic degradation, bactericidal activity, iron acquisition, and volatile pathways. These findings underscore the effectiveness and sensitivity of PGPg_finder in accurately identifying and comparing PGPT genes, highlighting both commonalities and variations across samples. The application of this pipeline has the potential to significantly facilitate the identification of plant growth-promoting microbes.

Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.

We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.

Patterns recovered in phylogenomic analysis of Candida auris and close relatives implicate broad environmental flexibility in Candida/Clavispora clade yeasts.

Fungal pathogens commonly originate from benign or non-pathogenic strains living in the natural environment. The recently emerged human pathogen, Candida auris, is one example of a fungus believed to have originated in the environment and recently transitioned into a clinical setting. To date, however, there is limited evidence about the origins of this species in the natural environment and when it began associating with humans. One approach to overcome this gap is to reconstruct phylogenetic relationships between (1) strains isolated from clinical and non-clinical environments and (2) between species known to cause disease in humans and benign environmental saprobes. C. auris belongs to the Candida/Clavispora clade, a diverse group of 45 yeast species including human pathogens and environmental saprobes. We present a phylogenomic analysis of the Candida/Clavispora clade aimed at understanding the ecological breadth and evolutionary relationships between an expanded sample of environmentally and clinically isolated yeasts. To build a robust framework for investigating these relationships, we developed a whole-genome sequence dataset of 108 isolates representing 18 species, including four newly sequenced species and 18 environmentally isolated strains. Our phylogeny, based on 619 orthologous genes, shows environmentally isolated species and strains interspersed with clinically isolated counterparts, suggesting that there have been many transitions between humans and the natural environment in this clade. Our findings highlight the breadth of environments these yeasts inhabit and imply that many clinically isolated yeasts in this clade could just as easily live outside the human body in diverse natural environments and vice versa.

Comparison of structural variant callers for massive whole-genome sequence data

BackgroundDetecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers.ResultsNotably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions.ConclusionsOur findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.

A revamped rat reference genome improves the discovery of genetic diversity in laboratory rats

The seventh iteration of the reference genome assembly for Rattus norvegicus-mRatBN7.2-corrects numerous misplaced segments and reduces base-level errors by approximately 9-fold and increases contiguity by 290-fold compared with its predecessor. Gene annotations are now more complete, improving the mapping precision of genomic, transcriptomic, and proteomics datasets. We jointly analyzed 163 short-read whole-genome sequencing datasets representing 120 laboratory rat strains and substrains using mRatBN7.2. We defined ∼20.0 million sequence variations, of which 18,700 are predicted to potentially impact the function of 6,677 genes. We also generated a new rat genetic map from 1,893 heterogeneous stock rats and annotated transcription start sites and alternative polyadenylation sites. The mRatBN7.2 assembly, along with the extensive analysis of genomic variations among rat strains, enhances our understanding of the rat genome, providing researchers with an expanded resource for studies involving rats.

Abstract 6054: UPPGRADER: a bioinformatics-based novel E3 ligase discovery platform

Abstract In Target Protein Degradation (TPD) field, conventional E3 ligases utilized in the clinic are limited to CRBN and VHL, which opens the need for exploration of novel E3 ligases for the advancement of TPD. To address this, we have developed a proprietary platform, UPPGRADER, for systematic discovery of novel E3 ligases based on -omics data including single cell RNA sequencing (scRNA-seq) and whole genome sequencing (WGS), in conjunction with proteomics data and bioinformatics tools.UPPGRADER enables systematic analyses on alterations occurred in all genomic areas including E3 ligase genes and potential target genes. For example, analyzing Pan-Cancer Analysis of Whole Genomes (PCAWG) data identified amino acid changes into Cysteine or Lysine, which can be targeted by covalent binders with cancer specificity. Through such analysis, we discovered several novel E3 ligases, including but not limited to FBXW7, with the categorization of the findings based on specific cancer types.At the single cell expression level, UPPGRADER provides the capability to assess gene expression levels of E3 ligases and target proteins by proprietary measurements based on gene detection rate. This system also allows assessment of E3 ligase-Target gene co-expression per single cell. In colorectal cancer, UPPGRADER has shown that the co-expression of E3 ligase FBXW7 and well-known colorectal cancer target (KRAS) increases from 1% (4 out of 334) in normal epithelial cells to 17% (2,967 out of 17,458) in cancerous epithelial cells, highlighting a significant change in their simultaneous expression patterns during cancer progression.UPPGRADER also identified E3 ligase X which showed high co-expression level with KRAS gene alongside universal expression properties and high cancer cell dependencies. For E3 ligase X, we developed novel small molecule binders (nM binding affinity) with bioavailability > 20% in animal pharmacokinetics. Bifunctional degraders utilizing these binders displayed BRD4 degradation in more than 20 cell lines with double to triple digit nM DC50 and DC90 values. Other targets (AURKA, CRBN and others) were potently degraded with anticipated mechanism of action (assessed by E3 ligase KO cell line and proteasomal inhibitors).To conclude, UPPGRADER is a comprehensive bioinformatics tool for discovering novel E3 ligases with biological validations. Through recent explosion of scRNA-seq and WGS datasets in different diseases and mechanism-based identification of additional molecular targets, UPPGRADER is poised to advance the field of TPD. Citation Format: Kyungsik Ha, Woojeung Song, Woochang Choi, Gi Bbeum Lee, Soohee Ryu, Jun Kyu Lee, Hwajin Lee. UPPGRADER: a bioinformatics-based novel E3 ligase discovery platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6054.

A New Cloud-Native Tool for Pharmacogenetic Analysis.

The advancement of next-generation sequencing (NGS) technologies provides opportunities for large-scale Pharmacogenetic (PGx) studies and pre-emptive PGx testing to cover a wide range of genotypes present in diverse populations. However, NGS-based PGx testing is limited by the lack of comprehensive computational tools to support genetic data analysis and clinical decisions. Bioinformatics utilities specialized for human genomics and the latest cloud-based technologies were used to develop a bioinformatics pipeline for analyzing the genomic sequence data and reporting PGx genotypes. A database was created and integrated in the pipeline for filtering the actionable PGx variants and clinical interpretations. Strict quality verification procedures were conducted on variant calls with the whole genome sequencing (WGS) dataset of the 1000 Genomes Project (G1K). The accuracy of PGx allele identification was validated using the WGS dataset of the Pharmacogenetics Reference Materials from the Centers for Disease Control and Prevention (CDC). The newly created bioinformatics pipeline, Pgxtools, can analyze genomic sequence data, identify actionable variants in 13 PGx relevant genes, and generate reports annotated with specific interpretations and recommendations based on clinical practice guidelines. Verified with two independent methods, we have found that Pgxtools consistently identifies variants more accurately than the results in the G1K dataset on GRCh37 and GRCh38. Pgxtools provides an integrated workflow for large-scale genomic data analysis and PGx clinical decision support. Implemented with cloud-native technologies, it is highly portable in a wide variety of environments from a single laptop to High-Performance Computing (HPC) clusters and cloud platforms for different production scales and requirements.

On the onset and dispersal of a major MDR TB clone among HIV-negative patients, Tunisia

BackgroundTo carry out a whole genome sequencing (WGS)-based investigation on the emergence and spread of the largest multidrug-resistant tuberculosis (MDR TB) outbreak that has been thriving among HIV-negative patients, Tunisia, since the early 2000s.MethodsWe performed phylogeographic analyses and molecular dating based on a WGS dataset representing 68 unique Mycobacterium tuberculosis isolates, covering almost the entire MDR TB outbreak for the time period 2001–2016.ResultsThe data indicate that the ancestor of the MDR TB outbreak emerged in the region of Bizerte, as early as 1974 (95% CI 1951–1985), from where it spread to other regions by 1992 (95% CI 1980–1996). Analysis of a minimum spanning tree based on core genome Multilocus Sequence Typing (cgMLST) uncovered the early spill-over of the fitness-compensated MDR TB strain from the prison into the general population. Indeed, cases with history of incarceration were found to be directly or indirectly linked to up to 22 new outbreak cases (32.35%) among the non-imprisoned population. By around 2008, the MDR TB outbreak strain had acquired additional resistance, leading to an XDR phenotype.ConclusionsWGS allowed refining our understanding of the emergence and evolution of the largest MDR TB outbreak in Tunisia, whose causative strain has been circulating silently for almost 26 years before. Our study lends further support to the critical role of prisons-related cases in the early spread of the outbreak among the general population. The shift to an XDR phenotype of such an epidemic clone prompts an urgent need to undertake drastic control measures.

Evaluating the efficacy of MEANGS for mitochondrial genome assembly of cartilaginous and ray-finned fish species.

The assembly of complete and circularized mitochondrial genomes (mitogenomes) is essential for population genetics, phylogenetics and evolution studies. Recently, Song etal. developed a seed-free tool called MEANGS for de novo mitochondrial assembly from whole genome sequencing (WGS) data in animals, achieving highly accurate and intact assemblies. However, the suitability of this tool for marine fish remains unexplored. Additionally, we have concerns regarding the overlap sequences in their original results, which may impact downstream analyses. In this Letter to the Editor, the effectiveness of MEANGS in assembling mitogenomes of cartilaginous and ray-finned fish species was assessed. Moreover, we also discussed the appropriate utilization of MEANGS in mitogenome assembly, including the implementation of the data-cut function and circular detection module. Our observations indicated that with the utilization of these modules, MEANGS efficiently assembled complete and circularized mitogenomes, even when handling large WGS datasets. Therefore, we strongly recommend users employ the data-cut function and circular detection module when using MEANGS, as the former significantly reduces runtime and the latter aids in the removal of overlapped sequences for improved circularization. Furthermore, our findings suggested that approximately 2× coverage of clean WGS data was sufficient for MEANGS to assemble mitogenomes in marine fish species. Moreover, due to its seed-free nature, MEANGS can be deemed one of the most efficient software tools for assembling mitogenomes from animal WGS data, particularly in studies with limited species or genetic background information.

Spatiotemporal DNA methylation dynamics shape megabase-scale methylome landscapes.

DNA methylation is an essential epigenetic mechanism that regulates cellular reprogramming and development. Studies using whole-genome bisulfite sequencing have revealed distinct DNA methylome landscapes in human and mouse cells and tissues. However, the factors responsible for the differences in megabase-scale methylome patterns between cell types remain poorly understood. By analyzing publicly available 258 human and 301 mouse whole-genome bisulfite sequencing datasets, we reveal that genomic regions rich in guanine and cytosine, when located near the nuclear center, are highly susceptible to both global DNA demethylation and methylation events during embryonic and germline reprogramming. Furthermore, we found that regions that generate partially methylated domains during global DNA methylation are more likely to resist global DNA demethylation, contain high levels of adenine and thymine, and are adjacent to the nuclear lamina. The spatial properties of genomic regions, influenced by their guanine-cytosine content, are likely to affect the accessibility of molecules involved in DNA (de)methylation. These properties shape megabase-scale DNA methylation patterns and change as cells differentiate, leading to the emergence of different megabase-scale methylome patterns across cell types.

Identification of eight genomic protective alleles for mitochondrial diabetes by Kinship-graph convolutional network.

Nearly 85% of maternally inherited diabetes and deafness (MIDD) are caused by the m.3243A>G mutation in the mitochondrial DNA. However, the clinical phenotypes of MIDD may also be influenced by the nuclear genome, this study aimed to investigate nuclear genome variants that influence clinical phenotypes associated with m.3243A>G mutation in MIDD based on whole-genome sequencing of the patients belonging to pedigrees. We analyzed a whole-genome sequencing (WGS) dataset from blood samples of 38 MIDD patients with the m.3243A > G mutation belonging to 10 pedigrees, by developing a Kinship-graph convolutional network approach, called Ki-GCN, integrated with the conventional genome-wide association study (GWAS) methods. We identified eight protective alleles in the nuclear genome that have protective effects against the onset of MIDD, related deafness, and also type 2 diabetes. Based on these eight protective alleles, we constructed an effective logistic regression model to predict the early or late onset of MIDD patients. There are protective alleles in the nuclear genome that are associated with the onset-age of MIDD patients and might also provide protective effects on the deafness derived from MIDD patients.

Investigation of ABO Gene Variants across More Than 60 Pig Breeds and Populations and Other Suidae Species Using Whole-Genome Sequencing Datasets.

Polymorphisms in the human ABO gene determine the major blood classification system based on the three well-known forms: A; B; and O. In pigs that carry only two main alleles in this gene (A and O), we still need to obtain a more comprehensive distribution of variants, which could also impact its function. In this study, we mined more than 500 whole-genome sequencing datasets to obtain information on the ABO gene in different Suidae species, pig breeds, and populations and provide (i) a comprehensive distribution of the A and O alleles, (ii) evolutionary relationships of ABO gene sequences across Suidae species, and (iii) an exploratory evaluation of the effect of the different ABO gene variants on production traits and blood-related parameters in Italian Large White pigs. We confirmed that allele O is likely under balancing selection, present in all Sus species investigated, without being fixed in any of them. We reported a novel structural variant in perfect linkage disequilibrium with allele O that made it possible to estimate the evolutionary time window of occurrence of this functional allele. We also identified two single nucleotide polymorphisms that were suggestively associated with plasma magnesium levels in pigs. Other studies can also be constructed over our results to further evaluate the effect of this gene on economically relevant traits and basic biological functions.

Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli.

Several bioinformatics genotyping algorithms are now commonly used to characterize antimicrobial resistance (AMR) gene profiles in whole-genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli, is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance and SRST2, run with the ResFinder database) and their outputs compared. For simulation tests where 3079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3076 (99.9 %) simulations, ABRicate for 3054 (99.2 %), ARIBA for 2783 (90.4 %) and SRST2 for 2108 (68.5 %). For simulation tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance identified the correct alleles in 35 338/46 318 (76.3 %) simulations, ABRicate identified them in 11 842/46 318 (25.6 %) simulations, ARIBA identified them in 1679/46 318 (3.6 %) simulations and SRST2 identified them in 2000/46 318 (4.3 %) simulations. In real data, across all methods, 1392/1818 (76 %) isolates had discrepant allele calls for at least 1 gene. In addition to highlighting areas for improvement in challenging scenarios, (e.g. identification of AMR genes at <10× coverage, identifying multiple closely related AMR genes present in the same sample), our evaluations identified some more systematic errors that could be readily soluble, such as repeated misclassification (i.e. naming) of genes as shorter variants of the same gene present within the reference resistance gene database. Such naming errors accounted for at least 2530/4321 (59 %) of the discrepancies seen in real data. Moreover, many of the remaining discrepancies were likely 'artefactual', with reporting of cut-off differences accounting for at least 1430/4321 (33 %) discrepants. Whilst we found that comparing outputs generated by running multiple algorithms on the same dataset could identify and resolve these algorithmic artefacts, the results of our evaluations emphasize the need for developing new and more robust genotyping algorithms to further improve accuracy and performance.

Time-resolved, integrated analysis of clonally evolving genomes.

Clonal genome evolution is a key feature of asexually reproducing species and human cancer development. While many studies have described the landscapes of clonal genome evolution in cancer, few determine the underlying evolutionary parameters from molecular data, and even fewer integrate theory with data. We derived theoretical results linking mutation rate, time, expansion dynamics, and biological/clinical parameters. Subsequently, we inferred time-resolved estimates of evolutionary parameters from mutation accumulation, mutational signatures and selection. We then applied this framework to predict the time of speciation of the marbled crayfish, an enigmatic, globally invasive parthenogenetic freshwater crayfish. The results predict that speciation occurred between 1986 and 1990, which is consistent with biological records. We also used our framework to analyze whole-genome sequencing datasets from primary and relapsed glioblastoma, an aggressive brain tumor. The results identified evolutionary subgroups and showed that tumor cell survival could be inferred from genomic data that was generated during the resection of the primary tumor. In conclusion, our framework allowed a time-resolved, integrated analysis of key parameters in clonally evolving genomes, and provided novel insights into the evolutionary age of marbled crayfish and the progression of glioblastoma.

ADSP Whole Genome Sequencing (WGS) Release 4 Data Update from Genome Center for Alzheimer’s Disease

AbstractBackgroundThe Genome Center for Alzheimer’s Disease (GCAD) coordinates the integration of all available Alzheimer’s disease (AD) relevant whole genome sequencing (WGS) data with the goal of identifying AD risk or protective genetic variants and eventual therapeutic targets. The WGS datasets are generated through collaboration between investigators from the Alzheimer’s Disease Sequencing Project (ADSP) and GCAD. With the goal of minimizing data heterogeneity, introduced by different sequencing protocols and assays, GCAD processes all samples using standardized pipelines and performs quality control (QC)/quality assurance (QA) checks.MethodsRaw sequencing data (FASTQs or BAMs) were aligned to GRCh38/hg38 by BWA, and variant calling and joint genotyping on single nucleotide variants (SNVs), insertions and deletions (indels), were done by GATK. Structural variants (SVs) were called per sample using the Smoove, Manta, and Strelka packages. Preliminary QA checks including sex check, contamination, and genotype concordance were performed followed by QC per ADSP protocol to evaluate the quality of samples and variants. To facilitate access and usage of massive joint‐genotype called VCF files, a compact version for storing variant info and sample genotypes only was released first.ResultsWe dropped 275 (0.7%) samples of poor coverage (&lt;20×), and we flagged 219 (0.6%) samples that were of borderline quality. As a result, the dataset (ADSP Release 4, 2022) includes 36,361 genomes from 40 diverse cohorts with 4 major ancestries: 16,573 Non‐Hispanic Whites, 11,358 Hispanics; 5,422 African Americans; and 2,802 Asians. Data are deeply sequenced (average genome coverage: 40x). All samples’ CRAMs and gVCFs from GATK were deposited into NIAGADS Data Sharing Service (DSS) (https://dss.niagads.org/) for public distribution. Joint‐genotyped called VCFs are undergoing a full QC/annotation process and will be made available. This joint‐genotyped called VCF contains &gt;362M bi‐allelic variants, &gt;58M multi‐allelic variants, with 95% of variants remaining after QC. SV calling is ongoing and data will be ready prior to the conference.ConclusionThe ADSP and GCAD generate high quality SNVs, indels and SV calls. Currently GCAD is preparing the next release of ∼60,000 more ancestrally‐diverse WGS samples sequenced primarily through the ADSP Follow‐Up Study, which we anticipate will be released in 2023 to greatly benefit the AD genetics community.

Widespread Deviant Patterns of Heterozygosity in Whole-Genome Sequencing Due to Autopolyploidy, Repeated Elements, and Duplication.

Most population genomic tools rely on accurate single nucleotide polymorphism (SNP) calling and filtering to meet their underlying assumptions. However, genomic complexity, resulting from structural variants, paralogous sequences, and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing (WGS) data with low (1.5×) to intermediate (4.8×) coverage for four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata). The analyses revealed that deviant SNPs accounted for 22% to 62% of all SNPs in salmonid datasets and approximately 11% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs, and could be partially associated with transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, underestimated pairwise FST values, and overestimated nucleotide diversity. Considering the widespread occurrence of deviant SNPs arising from a variety of sources, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths.

Identification of rare coding variants associated with Alzheimer’s disease.

AbstractBackgroundPrevious whole exome sequencing (WES) studies of Alzheimer’s disease (AD) have identified genome‐wide significant associations with rare variants in several novel genes and highlighted the need for investigations in larger samples. We combined WES and whole genome sequencing (WGS) data assembled by the Alzheimer Disease Sequencing Project (ADSP) to increase the power for detecting associations of AD with rare variants in gene coding regions.MethodWe developed an efficient computational pipeline to perform both pre‐merging and post‐merging genotype, variant and sample‐level quality control (QC) on WGS data containing 16,905 individuals and WES data for 20,504 individuals. The resultant sample included participants from European (EA, 11,279 AD cases, 8,924 controls), African American (AA, 2,757 AD cases, 4,336 controls), and Caribbean Hispanic (CH 1,438 AD cases, 3,256 controls) ancestries. We employed GENESIS to test association of AD with 250,465 bi‐allelic QC’d variants using a logistic model including covariates for age, sex, exome capture kit, read length, and ancestry principal components (PCs).ResultIn the total sample, variants from APOE and other known AD genes including SORL1 (P = 5.30×10‐7), NECTIN2 (P = 6.94×10‐7), and TREM2 R47H (P = 2.34×10‐13) crossed or neared the study‐wide significance (SWS) threshold of P = 2.00×10‐7 (0.05/250,465). SWS or borderline significant associations were also found with variants in several novel loci including TKTL2 (P = 2.35×10‐8), D2HGDH, (P = 1.09×10‐7), CECR1 (P = 4.02×10‐7), PDHA2 (P = 2.76×10‐7), GOLGA1 (P = 1.72×10‐7), CYLD (P = 1.84×10‐7), and RP11‐243M5.4 (P = 1.37×10‐7). PSEN1 missense mutation G206A (rs63750082) previously associated with early onset AD in the CH group, was significantly associated with late onset AD in the same population (P = 1.58×10‐13). SWS association of AD with APOE was observed in all groups. Significant associations were also observed with variants in SERPINB8 (P = 3.77×10‐9) and FAM171A (P = 7.20×10‐7) in EAs and TLR4 (P = 4.58×10‐7) in CH. Associations with several top‐ranked variants were replicated in the Alzheimer’s Disease Genetics Consortium GWAS dataset that were imputed using the TOPMed reference panel and ADSP 5K WGS dataset.ConclusionWe demonstrated that merging WGS and WES datasets can increase power to detect associations with rare coding variants in genes including ones previously implicated in AD (CYLD and TLR4) and early‐onset stroke (CECR1).

Ensemble methods for testing a global null

Abstract Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge are commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. In this paper, motivated by the success of ensemble learning methods for prediction or classification, we propose an ensemble framework for testing that mimics the spirit of random forests to deal with the challenges. Our ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power for global nulls. We apply the framework to four problems about global testing in different classes of alternatives arising from whole-genome sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and an analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests.