Whole genome sequencing allows prediction of certain phenotypic traits and genetic predispositions.
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Peer-reviewed literature demonstrates that whole-genome sequencing provides a comprehensive genomic view that enables the prediction of health-related phenotypes, clinical outcomes, and genetic predispositions or disease risks.
Summary Paragraph
Whole-genome sequencing (WGS) provides a comprehensive view of the genome, enabling detection of coding and non-coding genetic variation, and surveying complex regions which are difficult to genotype. Here, we report on whole-genome sequencing of 490,640 UK Biobank participants, building on previous genotyping
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and whole-exome sequencing (WES) efforts
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. This advance deepens our understanding of how genetics influences disease biology and further enhances the value of this open resource for the study of human biology and health. Coupling this dataset with rich phenotypic data, we surveyed within- and cross-ancestry genomic associations with health-related phenotypes and identified novel genetic and clinical insights. While most genome-wide significant associations with disease traits were primarily observed in Europeans, we also identified strong or novel signals in individuals of African and Asian ancestries. Deeper capture of exonic variation in both coding and UTR sequences, strengthened and surfaced novel insights relative to WES analyses. This landmark dataset, representing the largest collection of WGS and available to the UK Biobank research community, will enable advances into our understanding of the human genome, and facilitate the discovery of new diagnostics, therapeutics with higher efficacy and improved safety profile, and enable precision medicine strategies with the potential to improve global health.
Abstract Figure
Graphic summary.
Framework of the WGS UKB study. This figure captures the flow of this manuscript. We start with the collection of patient samples by UK Biobank and followed by the strategy taken to perform WGS. We continue with quality control performed on GraphTyper and DRAGEN datasets, followed by variant calling of SNPs, in/dels, and structural variants (SV). Thereafter we defined the phenotypes (binary and quantitative) associated with SV, SNPs and at the gene level (rare variant analysis) and conclude with the definition of five ancestry groups and collective association effect as a cross-ancestry meta-analysis.
Summary
Genetic privacy is an area of active research. While it is important to identify new risks, it is equally crucial to supply policymakers with accurate information based on scientific evidence. Recently, Lippert et al. (PNAS, 2017) investigated the status of genetic privacy using trait-predictions from whole genome sequencing. The authors sequenced a cohort of about 1000 individuals and collected a range of demographic, visible, and digital traits such as age, sex, height, face morphology, and a voice signature. They attempted to use the genetic features in order to predict those traits and re-identify the individuals from small pool using the trait predictions. Here, I report major flaws in the Lippert et al. manuscript. In short, the authors’ technique performs similarly to a simple baseline procedure, does not utilize the power of whole genome markers, uses technically wrong metrics, and finally does not really identify anyone.
Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10−16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10−16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases.
Whole‐genome sequencing is pivotal for elucidating the complex relationships between genotype and phenotype. However, its widespread application is hindered by the high sequencing depth and large sample sizes required, especially for genomic selection (GS) reliant on precise phenotype prediction from high‐density genotype data. To address this, DPImpute (Dual‐Phase Impute) is developed, an two‐step imputation pipeline enabling accurate whole‐genome SNP genotyping under ultra‐low coverage whole‐genome sequencing (ulcWGS) depths, small testing sample sizes, and limited reference populations. DPImpute achieved 98.06% SNP imputation accuracy with minimal testing samples (≤10), reference samples (≤100), and an ultra‐low sequencing depth of 0.3X, surpassing the accuracy of existing imputation methods. Moreover, this high accuracy is maintained across multi‐ancestry human populations. Remarkably, DPImpute demonstrated accurate SNP imputation from low‐coverage sequencing data from single blood cells and single blastocyst cells, highlighting its potential in embryo GS. To enhance the accessibility of DPImpute, a user‐friendly web server (https://agdb.ecenr.com/DPImpute/home) is developed and a Docker container for seamless implementation. In summary, DPImpute can significantly expedite breeding programs through precise and cost‐effective genotyping and serve as a valuable tool for diverse population genotyping, encompassing both human and animal studies.
Genome-wide sequencing allows for prediction of clinical treatment responses and outcomes by estimating genomic status. Here, we developed Genomic Status scan (GSscan), a long short-term memory (LSTM)-based deep-learning framework, which utilizes low-pass whole genome sequencing (WGS) data to capture genomic instability-related features. In this study, GSscan directly surveys homologous recombination deficiency (HRD) status independent of other existing biomarkers. In breast cancer, GSscan achieved an AUC of 0.980 in simulated low-pass WGS data, and obtained a higher HRD risk score in clinical BRCA-deficient breast cancer samples (p = 1.3 × 10−4, compared with BRCA-intact samples). In ovarian cancer, GSscan obtained higher HRD risk scores in BRCA-deficient samples in both simulated data and clinical samples (p = 2.3 × 10−5 and p = 0.039, respectively, compared with BRCA-intact samples). Moreover, HRD-positive patients predicted by GSscan showed longer progression-free intervals in TCGA datasets (p = 0.0011) treated with platinum-based adjuvant chemotherapy, outperforming existing low-pass WGS-based methods. Furthermore, GSscan can accurately predict HRD status using only 1 ng of input DNA and a minimum sequencing coverage of 0.02 × , providing a reliable, accessible, and cost-effective approach. In summary, GSscan effectively and accurately detected HRD status, and provide a broadly applicable framework for disease diagnosis and selecting appropriate disease treatment.
Introduction: Next-generation sequencing (NGS) data analysis has become an integral part of clinical genetic diagnosis, raising the question of variant prioritization. The Population Sampling Probability (PSAP) method has been developed to tackle the issue of variant prioritization in the exome of a single patient, by leveraging allele frequencies from population databases and a variant pathogenicity score. Methods: Here, we present Easy-PSAP, a completely new implementation of the PSAP method comprising two user-friendly and highly adaptable pipelines. Easy-PSAP allows the gene-based recalibration of any in silico pathogenicity prediction score compared to scores of variants seen in the general population, including popular scores like CADD or AlphaMissense. Easy-PSAP can evaluate genetic variants at the scale of a whole exome or a whole genome using information from the latest population and annotation databases. Results: Through simulations on synthetic disease exomes, we show that Easy-PSAP is able to rank more than 50% of causal pathogenic variants in the top 10 variants for an autosomal dominant model of transmission and in top 1 for an autosomal recessive model of transmission. Discussion: These findings, along with the accessibility of the pipeline to both researchers and clinicians, make Easy-PSAP a state-of-the-art tool for variant prioritization in NGS data that can continue to evolve as new frameworks and databases become available. Easy-PSAP is implemented in R a
Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8–81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and
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