A Genomics England haplotype reference panel and imputation of UK Biobank

We built a reference panel with 342 million autosomal variants using 78,195 individuals from the Genomics England (GEL) dataset, achieving a phasing switch error rate of 0.18% for European samples and imputation quality of r2 = 0.75 for variants with minor allele frequencies as low as 2 × 10−4 in white British samples. The GEL-imputed UK Biobank genome-wide association analysis identified 70% of associations found by direct exome sequencing (P < 2.18 × 10−11), while extending testing of rare variants to the entire genome. Coding variants dominated the rare-variant genome-wide association results, implying less disruptive effects of rare non-coding variants

CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer

We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of mul- ticellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled us- ing different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behaviour of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste\u2019s modu- lar simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modelling paradigms, two example applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second example is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue

Enhanced p53 Levels Are Involved in the Reduced Mineralization Capacity of Osteoblasts Derived from Shwachman–Diamond Syndrome Subjects

14noopenShwachman–Diamond syndrome (SDS) is a rare autosomal recessive disorder characterized by bone marrow failure, exocrine pancreatic insufficiency, and skeletal abnormalities, caused by loss-of-function mutations in the SBDS gene, a factor involved in ribosome biogenesis. By analyzing osteoblasts from SDS patients (SDS-OBs), we show that SDS-OBs displayed reduced SBDS gene expression and reduced/undetectable SBDS protein compared to osteoblasts from healthy subjects (H-OBs). SDS-OBs cultured in an osteogenic medium displayed a lower mineralization capacity compared to H-OBs. Whole transcriptome analysis showed significant differences in the gene expression of SDS-OBs vs. H-OBs, particularly in the ossification pathway. SDS OBs expressed lower levels of the main genes responsible for osteoblastogenesis. Of all downregulated genes, Western blot analyses confirmed lower levels of alkaline phosphatase and collagen type I in SDS-OBs than in H-OBs. Interestingly, SDS-OBs showed higher protein levels of p53, an inhibitor of osteogenesis, compared to H-OBs. Silencing of Tp53 was associated with higher collagen type I and alkaline phosphatase protein levels and an increase in SDS-OB mineralization capacity. In conclusion, our results show that the reduced capacity of SDS-OBs to mineralize is mediated, at least in part, by the high levels of p53 and highlight an important role of SBDS in osteoblast functions.openFrattini, Annalisa; Bolamperti, Simona; Valli, Roberto; Cipolli, Marco; Pinto, Rita Maria; Bergami, Elena; Frau, Maria Rita; Cesaro, Simone; Signo, Michela; Bezzerri, Valentino; Porta, Giovanni; Khan, Abdul Waheed; Rubinacci, Alessandro; Villa, IsabellaFrattini, Annalisa; Bolamperti, Simona; Valli, Roberto; Cipolli, Marco; Pinto, Rita Maria; Bergami, Elena; Frau, Maria Rita; Cesaro, Simone; Signo, Michela; Bezzerri, Valentino; Porta, Giovanni; Khan, Abdul Waheed; Rubinacci, Alessandro; Villa, Isabell

Cognitive Targeted Prostate Biopsy Alone for Diagnosing Clinically Significant Prostate Cancer in Selected Biopsy-Naïve Patients: Results from a Retrospective Pilot Study

: (1) Background: To identify a particular setting of biopsy-naïve patients in which it would be reasonable to offer only cognitive targeted prostate biopsy (PBx) with a transrectal approach. (2) Methods: We designed an observational retrospective pilot study. Patients with a prostatic specific antigen (PSA) level > 10 ng/mL, either a normal or suspicious digital rectal examination (DRE), and a lesion with a PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone were included. All patients underwent a transrectal PBx, including both systematic and targeted samples. The detection rate of clinically significant prostate cancer (csPCa) (Gleason Score ≥ 7) was chosen as the primary outcome. We described the detection rate of csPCa in systematic PBx, targeted PBx, and overall PBx. (3) A total of 92 patients were included. Prostate cancer was detected in 84 patients (91.30%) with combined biopsies. A csPCa was diagnosed in all positive cases (100%) with combined biopsies. Systematic PBxs were positive in 80 patients (86.96%), while targeted PBxs were positive in 84 men (91.30%). Targeted PBx alone would have allowed the diagnosis of csPCa in all positive cases; systematic PBx alone would have missed the diagnosis of 8/84 (9.52%) csPCa cases (4 negative patients and 4 not csPCa) (p = 0.011). (4) Conclusions: Cognitive targeted PBx with a transrectal approach could be offered alone to diagnose csPCa in biopsy-naïve patients with PSA ≥ 10 ng/mL, either normal or suspicious DRE, and a lesion with PI-RADS score ≥ 4 in the postero-medial or postero-lateral peripheral zone

Exploring the Multifactorial Landscape of Penile Cancer: A Comprehensive Analysis of Risk Factors

: Penile cancer, while rare, is a critical public health issue due to its profound impact on patients and the complexities of its management. The disease's multifactorial etiology includes risk factors such as HPV infection, poor hygiene, smoking, genetic predispositions, and socioeconomic determinants. This article provides a comprehensive review and analysis of these diverse risk factors, aiming to enhance understanding of the disease's underlying causes. By elucidating these factors, the article seeks to inform and improve prevention strategies, early detection methods, and therapeutic interventions. A nuanced grasp of the multifactorial nature of penile cancer can enable healthcare professionals to develop more effective approaches to reducing incidence rates and improving patient outcomes

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4x mean depth) and deep whole exome (30-40x mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R2 concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% R2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF≥1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of ∼90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations

Genotype imputation using the Positional Burrows Wheeler Transform

AbstractGenotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods.Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model.Using the HRC reference panel, which has ~65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.Author summaryGenome-wide association studies (GWAS) typically use microarray technology to measure genotypes at several hundred thousand positions in the genome. However reference panels of genetic variation consist of haplotype data at &gt;100 fold more positions in the genome. Genotype imputation makes genotype predictions at all the reference panel sites using the GWAS data. Reference panels are continuing to grow in size and this improves accuracy of the predictions, however methods need to be able to scale to increased size. We have developed a new version of the popular IMPUTE software than can handle referenece panels with millions of haplotypes, and has better performance than other published approaches. A notable property of the new method is that it scales sub-linearly with reference panel size. Keeping the number of imputed markers constant, a 100 fold increase in reference panel size requires less than twice the computation time.</jats:sec

Genotype imputation using the Positional Burrows Wheeler Transform

Genotype imputation is the process of predicting unobserved genotypes in a sample of individuals using a reference panel of haplotypes. In the last 10 years reference panels have increased in size by more than 100 fold. Increasing reference panel size improves accuracy of markers with low minor allele frequencies but poses ever increasing computational challenges for imputation methods. Here we present IMPUTE5, a genotype imputation method that can scale to reference panels with millions of samples. This method continues to refine the observation made in the IMPUTE2 method, that accuracy is optimized via use of a custom subset of haplotypes when imputing each individual. It achieves fast, accurate, and memory-efficient imputation by selecting haplotypes using the Positional Burrows Wheeler Transform (PBWT). By using the PBWT data structure at genotyped markers, IMPUTE5 identifies locally best matching haplotypes and long identical by state segments. The method then uses the selected haplotypes as conditioning states within the IMPUTE model. Using the HRC reference panel, which has ∼65,000 haplotypes, we show that IMPUTE5 is up to 30x faster than MINIMAC4 and up to 3x faster than BEAGLE5.1, and uses less memory than both these methods. Using simulated reference panels we show that IMPUTE5 scales sub-linearly with reference panel size. For example, keeping the number of imputed markers constant, increasing the reference panel size from 10,000 to 1 million haplotypes requires less than twice the computation time. As the reference panel increases in size IMPUTE5 is able to utilize a smaller number of reference haplotypes, thus reducing computational cost.</jats:p

The kinesin Eg5 inhibitor K858 induces apoptosis and reverses the malignant invasive phenotype in human glioblastoma cells

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor and the current chemotherapeutic options for GBM are limited to temozolomide. Recently, inhibitors of kinesin spindle protein Eg5 have shown pronounced antitumor activity and our group has demonstrated that one of these inhibitors of kinesin Eg5, named K858, exerts important anti-proliferative and apoptotic effects on breast cancer cells. Since GBM cells usually express high levels of kinesin Eg5, we tested the effect of K858 on two human GBM cell lines (U-251 and U-87) and found that K858 could inhibit cell growth, induce apoptosis, revert epithelial-mesenchymal transition and inhibit migration in both cell lines. However, at the same time, K858 increased survivin expression, an anti-apoptotic molecule; so, down-regulating survivin with the specific inhibitor YM155, we obtained an important increase of K858-dependent apoptosis. This indicated that the anti-tumor activity of K858 on GBM was limited by the over-expression of survivin and that the negative regulation of this protein could sensitize tumor cells to K858-induced apoptosis. These data support the choice of kinesin Eg5 as target of new therapeutic approaches for GBM; in particular, K858 has been demonstrated to be a potent inhibitor of replication, inducer of apoptosis and negative regulator of the invasive phenotype for GBM cells

XSI—a genotype compression tool for compressive genomics in large biobanks

AbstractMotivationGeneration of genotype data has been growing exponentially over the last decade. With the large size of recent datasets comes a storage and computational burden with ever increasing costs. To reduce this burden, we propose XSI, a file format with reduced storage footprint that also allows computation on the compressed data and we show how this can improve future analyses.ResultsWe show that xSqueezeIt (XSI) allows for a file size reduction of 4-20× compared with compressed BCF and demonstrate its potential for ‘compressive genomics’ on the UK Biobank whole-genome sequencing genotypes with 8× faster loading times, 5× faster run of homozygozity computation, 30× faster dot products computation and 280× faster allele counts.Availability and implementationThe XSI file format specifications, API and command line tool are released under open-source (MIT) license and are available at https://github.com/rwk-unil/xSqueezeItSupplementary informationSupplementary data are available at Bioinformatics online.</jats:sec