Exome Datasets Research Articles

NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

Exome Sequencing of a Blastomycosis Case–Control Cohort From Manitoba and Northwestern Ontario, Canada

ABSTRACTBackgroundBlastomycosis is a pulmonary disease caused by Blastomyces spp., a group of pathogenic dimorphic fungi endemic to a number of geographic regions, specifically Manitoba and northwestern Ontario, Canada. Immunosuppression is a major risk factor affecting disease susceptibility, yet host immunity is not well understood. Genetic immunodeficiencies can also influence disease, with variants in IL6, GATA2 and VDBP shown to influence susceptibility. Additional genetic factors in disease susceptibility and severity remain undetected. Our study seeks to identify potential genetic risk factors in a blastomycosis case–control cohort from Manitoba and northwestern Ontario, Canada.MethodsExomes from 18 blastomycosis cases and 9 controls were sequenced, variants were identified and filtered for accuracy and quality. We performed candidate gene prioritisation and variant aggregation to identify genetic associations and explored the full exome dataset.ResultsNinety‐nine genetic variants in 42 candidate genes were identified in the exome dataset. No variants associated with susceptibility were identified in a single‐variant analysis although two non‐synonymous variants in TYK2 were enriched among cases suggesting a possible role in susceptibility. Gene‐based association analysis found variants in TLR1 enriched in controls (p = 0.024) suggesting a possible protective effect. Gene cluster analysis identified genetic variants in genes of chromatin remodelling, proteasome and intraflagellar transport significantly enriched in cases (false discovery rates < 14%).ConclusionsThe findings in this study show novel associations with blastomycosis susceptibility. A better understanding of host immunity and genetic predisposition to Blastomyces infection can help to inform clinical practice for improved outcomes.

Ancestry specific distribution of LPA Kringle IV-Type-2 genetic variants highlight associations to apo(a) copy number, glucose, and hypertension.

High Lp(a) levels contribute to atherosclerotic cardiovascular disease and are tightly regulated by the LPA gene. Lp(a) levels have an inverse correlation with LPA Kringle IV Type-2 (KIV-2) copy number (CN). Black (B) and Hispanic (H) individuals exhibit higher levels of Lp(a), and rates of CVD compared to non-Hispanic Whites (NHW). Therefore, we investigated genetic variations in the LPA KIV-2 region across three ancestries and their associations with metabolic risk factors. Using published pipelines, we analyzed a multi-ethnic whole exome dataset comprising 3,817 participants from the Washington Heights and Inwood Columbia Aging Project (WHICAP): 886 [NHW (23%), 1,811 Caribbean (C) H (47%), and 1,120 B individuals (29%). Rare and common variants (alternative allele carrier frequency, CF < 0.01 or > 0.99 and 0.01 < CF < 0.99, respectively) were identified and KIV-2 CN estimated. The associations of variants and CN with history of heart disease, hypertension (HTN), stroke, lipid levels and clinical diagnosis of Alzheimer's disease (AD) was assessed. A small pilot provided in-silico validation of study findings. We report 1421 variants in the LPA KIV-2 repeat region, comprising 267 exonic and 1154 intronic variants. 61.4% of the exonic variants have not been previously described. Three novel exonic variants significantly increase the risk of HTN across all ethnic groups: 4785-C/A (frequency = 78%, odds ratio [OR] = 1.45, p = 0.032), 727-T/C (frequency = 96%, OR = 2.11, p = 0.032), and 723-A/G (frequency = 96%, OR = 1.97, p = 0.038). Additionally, six intronic variants showed associations with HTN: 166-G/A, 387-G/C, 402-G/A, 4527-A/T, 4541-G/A, and 4653-A/T. One intronic variant, 412-C/T, was associated with decreased blood glucose levels (frequency = 72%, β = -14.52, p = 0.02). Three of the associations were not affected after adjusting for LPA KIV-2 CN: 412-C/T (β = -14.2, p = 0.03), 166-G/A (OR = 1.41, p = 0.05), and 387-G/C (OR = 1.40, p = 0.05). KIV CN itself was significantly associated with 314 variants and was negatively correlated with plasma total cholesterol levels. In three ancestry groups, we identify novel rare and common LPA KIV-2 region variants. We report new associations of variants with HTN and Glucose levels. These results underscore the genetic complexity of the LPA KIV-2 region in influencing cardiovascular and metabolic health, suggesting potential genetic regulation of pathways that can be studied for research and therapeutic interventions. Lp(a) levels are mostly controlled by the LPA gene and are higher in Blacks and Hispanics. Novel LPA KIV-2 variants found in three ancestry groups, including data on Caribbean Hispanics, show strong positive associations to hypertension and negative associations to glucose levels. Further characterization of these variants and identifying links to disease can help precision medicine efforts to understand disease mechanisms in all populations.

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing datasets.

Spinal muscular atrophy (SMA) is a genetic disorder that causes progressive degeneration of lower motor neurons and the subsequent loss of muscle function throughout the body. It is the second most common recessive disorder in individuals of European descent and is present in all populations. Accurate tools exist for diagnosing SMA from genome sequencing data. However, there are no publicly available tools for GRCh38-aligned data from panel or exome sequencing assays which continue to be used as first line tests for neuromuscular disorders. This deficiency creates a critical gap in our ability to diagnose SMA in large existing rare disease cohorts, as well as newly sequenced exome and panel datasets. We therefore developed and extensively validated a new tool - SMA Finder - that can diagnose SMA not only in genome, but also exome and panel sequencing samples aligned to GRCh37, GRCh38, or T2T-CHM13. It works by evaluating aligned reads that overlap the c.840 position of SMN1 and SMN2 in order to detect the most common molecular causes of SMA. We applied SMA Finder to 16,626 exomes and 3,911 genomes from heterogeneous rare disease cohorts sequenced at the Broad Institute Center for Mendelian Genomics as well as 1,157 exomes and 8,762 panel sequencing samples from Tartu University Hospital. SMA Finder correctly identified all 16 known SMA cases and reported nine novel diagnoses which have since been confirmed by clinical testing, with another four novel diagnoses undergoing validation. Notably, out of the 29 total SMA positive cases, 23 had an initial clinical diagnosis of muscular dystrophy, congenital myasthenic syndrome, or myopathy. This underscored the frequency with which SMA can be misdiagnosed as other neuromuscular disorders and confirmed the utility of using SMA Finder to reanalyze phenotypically diverse neuromuscular disease cohorts. Finally, we evaluated SMA Finder on 198,868 individuals that had both exome and genome sequencing data within the UK Biobank (UKBB) and found that SMA Finder's overall false positive rate was less than 1 / 200,000 exome samples, and its positive predictive value (PPV) was 97%. We also observed 100% concordance between UKBB exome and genome calls. This analysis showed that, even though it is located within a segmental duplication, the most common causal variant for SMA can be detected with comparable accuracy to monogenic disease variants in non-repetitive regions. Additionally, the high PPV demonstrated by SMA Finder, the existence of treatment options for SMA in which early diagnosis is imperative for therapeutic benefit, as well as widespread availability of clinical confirmatory testing for SMA, warrants the addition of SMN1 to the ACMG list of genes with reportable secondary findings after genome and exome sequencing.

NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling.

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

Detection of ATXN2 Expansions in an Exome Dataset: An Underdiagnosed Cause of Parkinsonism.

CAG-repeat expansions in Ataxin 2 (ATXN2) are known to cause spinocerebellar ataxia type 2 (SCA2), but CAA interrupted expansions may also result in autosomal dominant Parkinson's disease (AD PD). However, because of technical limitations, such expansions are not explored in whole exome sequencing (WES) data. To identify ATXN2 expansions using WES data from PD cases. We explored WES data from a cohort of 477 index cases with PD using ExpansionHunter (Illumina DRAGEN Bio-IT Platform, San Diego, CA). Putative expansions were confirmed by combining polymerase chain reaction and fragment length analysis followed by sub-cloning and sequencing methods. Using ExpansionHunter, we identified three patients from two families with AD PD carrying either ATXN2 22/39 or 22/37 repeats, both interrupted by four CAA repeats. These findings demonstrate the usefulness of WES to detect pathogenic CAG repeat expansions, which were found in 1.7% of AD PD in the ATXN2 gene in our exome dataset.

Exome-wide screening identifies novel rare risk variants for bone mineral density.

BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.

Evaluating High-Confidence Genes in Conotruncal Cardiac Defects by Gene Burden Analyses.

Background In nonsyndromic conotruncal cardiac defects, the use of next-generation sequencing for clinical diagnosis is increasingly adopted, but gene-disease associations in research are only partially translated to diagnostic panels, suggesting a need for evidence-based consensus. Methods and Results In an exome data set of 245 patients with conotruncal cardiac defects, we performed burden analysis on a high-confidence congenital heart disease gene list (n=132) with rare (<0.01%) and ultrarare (absent in the Genome Aggregation Database) protein-altering variants. Overall, we confirmed an excess of rare variants compared with ethnicity-matched controls and identified 2 known genes (GATA6, NOTCH1) and 4 candidate genes supported by the literature (ANKRD11, DOCK6, NPHP4, and STRA6). Ultrarare variant analysis was performed in combination with 3 other published studies (n=1451) and identified 3 genes (FLT4, NOTCH1, TBX1) to be significant, whereas a subgroup analysis involving 391 Chinese subjects identified only GATA6 as significant. Conclusions We suggest that these significant genes in our rare and ultrarare burden analyses warrant prioritization for clinical testing implied for rare inherited and de novo variants. Additionally, associations on ClinVar for these genes were predominantly variants of uncertain significance. Therefore, a more stringent assessment of gene-disease associations in a larger and ethnically diverse cohort is required to be prudent for future curation of conotruncal cardiac defect genes.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Gene‐based polygenic score analysis identifies novel immune genes with deleterious variants that associate with Alzheimer’s disease

AbstractBackgroundIn the Alzheimer’s disease sequencing project (ADSP) whole exome dataset, only the well‐known TREM2.pR47H variant shows genome‐wide significance by conventional single variant analysis. To identify novel AD genes related to TREM2, we used gene‐based polygenic score analysis (PGSA) to evaluate an immune, temporal cortical co‐expression network with 229 genes including TREM2.MethodThe most recent whole exome variant call files from the ADSP (NG00067.v7) were subjected to stringent quality control. Post‐QC samples were divided into discovery (4174 samples sequenced at Broad), test (3299 samples sequenced at WashU), and follow‐up (2273 samples sequenced at Baylor) sets. The pruned variants (r2 &lt; 0.2) with a minor allele count over 20 in each gene were analyzed by Broad/WashU PGSA thereby obtaining P‐values (gPbr.wa) for each gene in WashU samples. To assess significance in independent Baylor samples, cumulative Broad/Baylor PGSA was performed using a novel approach in which variants were stratified by both gene (gPbr.wa) and variant (Broad.P).ResultAfter excluding TREM2, the five variants with gPbr.wa ≤ 0.08 and Broad.P ≤ 0.03 showed significant association in Broad/Baylor PGSA. One of these variants showed no evidence of association with AD in the combined dataset. Polygenic scores for the other four variants, which were in three genes, showed significant association in Baylor samples, and significant, essentially identical association in WashU samples. Polygenic scores for nine additional variants in these genes (0.03&gt; Broad.P ≤ 0.13) also showed significant, essentially identical association in Baylor and WashU samples. The association of these 13 variants with AD was driven primarily by six deleterious, protein‐altering variants with minor allele frequencies less than 1%, but the other 7 variants also contributed.ConclusionUsing gene‐based polygenic score analysis to analyze an immune co‐expression network, we identify three novel immune genes with actionable nonsynonymous and stopgain variants that show significant, replicable association with AD in the most recent release of the ADSP whole exome dataset.

Hgtseq: A Standard Pipeline to Study Horizontal Gene Transfer

Horizontal gene transfer (HGT) is well described in prokaryotes: it plays a crucial role in evolution, and has functional consequences in insects and plants. However, less is known about HGT in humans. Studies have reported bacterial integrations in cancer patients, and microbial sequences have been detected in data from well-known human sequencing projects. Few of the existing tools for investigating HGT are highly automated. Thanks to the adoption of Nextflow for life sciences workflows, and to the standards and best practices curated by communities such as nf-core, fully automated, portable, and scalable pipelines can now be developed. Here we present nf-core/hgtseq to facilitate the analysis of HGT from sequencing data in different organisms. We showcase its performance by analysing six exome datasets from five mammals. Hgtseq can be run seamlessly in any computing environment and accepts data generated by existing exome and whole-genome sequencing projects; this will enable researchers to expand their analyses into this area. Fundamental questions are still open about the mechanisms and the extent or role of horizontal gene transfer: by releasing hgtseq we provide a standardised tool which will enable a systematic investigation of this phenomenon, thus paving the way for a better understanding of HGT.

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

Whole Exome Sequencing Study Identifies Novel Rare Risk Variants for Habitual Coffee Consumption Involved in Olfactory Receptor and Hyperphagia.

Habitual coffee consumption is an addictive behavior with unknown genetic variations and has raised public health issues about its potential health-related outcomes. We performed exome-wide association studies to identify rare risk variants contributing to habitual coffee consumption utilizing the newly released UK Biobank exome dataset (n = 200,643). A total of 34,761 qualifying variants were imported into SKAT to conduct gene-based burden and robust tests with minor allele frequency <0.01, adjusting the polygenic risk scores (PRS) of coffee intake to exclude the effect of common coffee-related polygenic risk. The gene-based burden and robust test of the exonic variants found seven exome-wide significant associations, such as OR2G2 (PSKAT = 1.88 × 10−9, PSKAT-Robust = 2.91 × 10−17), VEZT1 (PSKAT = 3.72 × 10−7, PSKAT-Robust = 1.41 × 10−7), and IRGC (PSKAT = 2.92 × 10−5, PSKAT-Robust = 1.07 × 10−7). These candidate genes were verified in the GWAS summary data of coffee intake, such as rs12737801 (p = 0.002) in OR2G2, and rs34439296 (p = 0.008) in IRGC. This study could help to extend genetic insights into the pathogenesis of coffee addiction, and may point to molecular mechanisms underlying health effects of habitual coffee consumption.

Efficient DNA/RNA extraction from tarsal plates by SK mill, a freeze-crush apparatus.

The tarsal plate is an eyelid tissue that maintains lid structure from inside the upper/lower eyelids, and it surrounds the meibomian glands and supports their unique secretion mechanism. Sebaceous carcinoma, a malignant eyelid tumour, can sometimes develop from the meibomian glands and is usually excised together with the tarsal plate during surgery, so the tarsal plate serves as a control research tissue. However, since the plate is thick, hard and heterogeneous with few cells, obtaining enough genomic DNA and/or total RNA is often difficult. Therefore, we attempted to establish an efficient protocol to obtain DNA and RNA simultaneously by comparing the combinations of homogenization (mortar/pestle, pellet pestle or SK mill) and purification (organic solvent or spin column) methods using rabbit tarsal plates. Based on the yield, quality and hands-on time, the SK mill and spin column was found to be the most efficient combination. We then applied the established protocol to extract DNA/RNA from six human tarsal-plate samples and succeeded in generating high-quality exome and transcriptome datasets via a next-generation sequencer with sufficient coverage and meibomian gland-specific expression of representative genes, respectively. Our new findings will provide ideal reference data for future genetic and gene-expression studies of sebaceous carcinoma.

B7-H3 as a Therapeutic Target in Advanced Prostate Cancer

BackgroundB7-H3 is a cell surface immunomodulatory glycoprotein overexpressed in prostate cancers (PCs). Understanding its longitudinal expression at emergence of castration resistance and association with tumour genomics are critical to the development of and patient selection for B7-H3 targeted therapies. ObjectiveTo characterise B7-H3 expression in same-patient hormone-sensitive (HSPC) and castration-resistant (CRPC) PC biopsies, associating this with PC genomics, and to evaluate the antitumour activity of an anti–B7-H3 antibody-drug conjugate (ADC) in human CRPC in vitro and in vivo. Design, setting, and participantsWe performed immunohistochemistry and next-generation sequencing on a cohort of 98 clinically annotated CRPC biopsies, including 72 patients who also had HSPC biopsies for analyses. We analysed two CRPC transcriptome and exome datasets, and PC scRNASeq datasets. PC organoids (patient-derived xenograft [PDX]-derived organoids [PDX-Os]) were derived from PDXs generated from human CRPC biopsies. Outcome measurements and statistical analysisWe evaluated B7-H3 mRNA expression in relation to a panel of 770 immune-related genes, compared B7-H3 protein expression between same-patient HSPC and CRPC biopsies, determined associations with PC genomic alterations, and evaluated the antitumour activity of DS-7300a, a topoisomerase-1 inhibitor payload anti–B7-H3 ADC, in human PC cell lines, organoids (PDX-Os), and xenografts (PDXs) of different histologies, B7-H3 expressions, and genomics. Results and limitationsB7-H3 was among the most highly expressed immunomodulatory genes in CRPCs. Most CRPCs (93%) expressed B7-H3, and in patients who developed CRPC, B7-H3 expression was frequently expressed at the time of HSPC diagnosis (97%). Conversion from B7-H3 positive to negative, or vice versa, during progression from HSPC to CRPC was uncommon. CRPC with neuroendocrine features were more likely to be B7-H3 negative (28%) than adenocarcinomas. B7-H3 is overexpressed in tumours with defective DNA repair gene (ATM and BRCA2) alterations and is associated with ERG expression, androgen receptor (AR) expression, and AR activity signature. DS7300a had antitumour activity against B7-H3 expressing human PC models including cell lines, PDX-Os, and PDXs of adenocarcinoma and neuroendocrine histology. ConclusionsThe frequent overexpression of B7-H3 in CRPC compared with normal tissue and other B7 family members implicates it as a highly relevant therapeutic target in these diseases. Mechanisms driving differences in B7-H3 expression across genomic subsets warrant investigation for understanding the role of B7-H3 in cancer growth and for the clinical development of B7-H3 targeted therapies. Patient summaryB7-H3, a protein expressed on the surface of the most lethal prostate cancers, in particular those with specific mutations, can be targeted using drugs that bind B7-H3. These findings are relevant for the development of such drugs and for deciding which patients to treat with these new drugs.

Bi-Allelic COQ4 Variants Cause Adult-Onset Ataxia-Spasticity Spectrum Disease.

COQ4 codes for a mitochondrial protein required for coenzyme Q10 (CoQ10 ) biosynthesis. Autosomal recessive COQ4-associated CoQ10 deficiency leads to an early-onset mitochondrial multi-organ disorder. In-house exome and genome datasets (n=14,303) were screened for patients with bi-allelic variants in COQ4. Work-up included clinical characterization and functional studies in patient-derived cell lines. Six different COQ4 variants, three of them novel, were identified in six adult patients from four different families. Three patients had a phenotype of hereditary spastic paraparesis, two sisters showed a predominant cerebellar ataxia, and one patient had mild signs of both. Studies in patient-derived fibroblast lines revealed significantly reduced amounts of COQ4 protein, decreased CoQ10 concentrations, and elevated levels of the metabolic intermediate 6-demethoxyubiquinone. We report bi-allelic variants in COQ4 causing an adult-onset ataxia-spasticity spectrum phenotype and a disease course much milder than previously reported. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Evaluating the role of rare genetic variation in sleep duration

ObjectivesTo explore the roles of rare and high-impact variants in sleep duration. DesignBased on the recently released UK Biobank 200k exome dataset, an exome-wide association study was conducted to detect rare variants (minor allele frequency <0.01) contributing to sleep duration. Variant annotations were performed by the software tool ANNOVAR. Gene-based burden tests of sleep duration were conducted by the SKAT R-package. After quality control, 137,047 subjects were included in this study. CAUSALdb database was used to explore the related mental traits of identified genes. ResultsWe detected 730,572 variants with MAF < 1%, including 3873 frameshift variants, 3977 nonframeshift variants, 449,632 nonsynonymous variants, 1293 startloss variants, 10,254 stopgain variants, 413 stoploss variants, 261,130 synonymous variants, and 3102 variants are annotated as unknown. The burden test of exonic variants detected two exome-wide significant associations for sleep duration including TMIE at 3p21.31 (PBonferroni adjusted = 0.015) and ZIC2 at 13q32.3 (PBonferroni adjusted = 0.047). There are only nonsynonymous contained in TMIE; as for ZIC2, we detected 2 annotations of variants: nonsynonymous (PBonferroni adjusted =2.04 × 10−4) and nonframeshift (PBonferroni adjusted =0.85). TMIE and ZIC2 were reported to be associated with several mental traits, such as chronotype, depression, and brain natriuretic peptide in published study. ConclusionThis study reported 2 novel candidate genes for a sleep duration, supporting the roles of rare genetic variants in the regulation of sleep duration.

Adult-Onset Neurodegeneration in Nucleotide Excision Repair Disorders (NERDND ): Time to Move Beyond the Skin.

Variants in genes of the nucleotide excision repair (NER) pathway have been associated with heterogeneous clinical presentations ranging from xeroderma pigmentosum to Cockayne syndrome and trichothiodystrophy. NER deficiencies manifest with photosensitivity and skin cancer, but also developmental delay and early-onset neurological degeneration. Adult-onset neurological features have been reported in only a few xeroderma pigmentosum cases, all showing at least mild skin manifestations. The aim of this multicenter study was to investigate the frequency and clinical features of patients with biallelic variants in NER genes who are predominantly presenting with neurological signs. In-house exome and genome datasets of 14,303 patients, including 3543 neurological cases, were screened for deleterious variants in NER-related genes. Clinical workup included in-depth neurological and dermatological assessments. We identified 13 patients with variants in ERCC4 (n=8), ERCC2 (n=4), or XPA (n=1), mostly proven biallelic, including five different recurrent and six novel variants. All individuals had adult-onset progressive neurological deterioration with ataxia, dementia, and frequently chorea, neuropathy, and spasticity. Brain magnetic resonance imaging showed profound global brain atrophy in all patients. Dermatological examination did not show any skin cancer or pronounced ultraviolet damage. We introduce NERDND as adult-onset neurodegeneration (ND ) within the spectrum of autosomal recessive NER disorders (NERD). Our study demonstrates that NERDND is probably an underdiagnosed cause of neurodegeneration in adulthood and should be considered in patients with overlapping cognitive and movement abnormalities. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Eph and Ephrin Variants in Malaysian Neural Tube Defect Families.

Neural tube defects (NTDs) are common birth defects with a complex genetic etiology. Mouse genetic models have indicated a number of candidate genes, of which functional mutations in some have been found in human NTDs, usually in a heterozygous state. This study focuses on Ephs-ephrins as candidate genes of interest owing to growing evidence of the role of this gene family during neural tube closure in mouse models. Eph-ephrin genes were analyzed in 31 Malaysian individuals comprising seven individuals with sporadic spina bifida, 13 parents, one twin-sibling and 10 unrelated controls. Whole exome sequencing analysis and bioinformatic analysis were performed to identify variants in 22 known Eph-ephrin genes. We reported that three out of seven spina bifida probands and three out of thirteen family members carried a variant in either EPHA2 (rs147977279), EPHB6 (rs780569137) or EFNB1 (rs772228172). Analysis of public databases shows that these variants are rare. In exome datasets of the probands and parents of the probands with Eph-ephrin variants, the genotypes of spina bifida-related genes were compared to investigate the probability of the gene–gene interaction in relation to environmental risk factors. We report the presence of Eph-ephrin gene variants that are prevalent in a small cohort of spina bifida patients in Malaysian families.

The effect of LPA Thr3888Pro on lipoprotein(a) and coronary artery disease is modified by the LPA KIV-2 variant 4925G>A

Background and aimsHigh lipoprotein(a) [Lp(a)] concentrations are associated with increased coronary artery disease (CAD) risk. Lp(a) is regulated mainly genetically by the LPA gene but involved genetic variants have not been fully elucidated. Improved understanding of the entanglements of genetic Lp(a) regulation may enhance genetic prediction of Lp(a) and CAD risk. We investigated an interaction between the well-known LPA missense SNP rs41272110 (known as Thr3888Pro) and the frequent LPA splicing mutation KIV-2 4925G>A. MethodsEffects on Lp(a) concentrations were investigated by multiple quantile regression in the German Chronic Kidney Disease (GCKD) study, KORA-F3 and KORA-F4 (ntotal = 10,405) as well as in the UK Biobank (UKB) 200k exome dataset (n = 173,878). The impact of the interaction on CAD risk was assessed by survival analysis in UKB. ResultsWe observed a significant SNP-SNP interaction in all studies (p = 1.26e-05 to 3.03e-04). In quantile regression analysis, rs41272110 as a predictor shows no impact on Lp(a) (β = −0.06 [-0.79; 0.68], p = 0.879), but in a joint model including both SNPs as predictors, rs41272110 is associated with markedly higher Lp(a) (β = +9.40 mg/dL [6.45; 12.34], p = 4.07e-10). Similarly, rs41272110 shows no effect on CAD in UKB (HR = 1.01 [0.97; 1.04], p = 0.731), while rs41272110 carriers not carrying 4925G>A show an increased CAD risk (HR = 1.10 [1.04; 1.16], p = 6.9e-04). This group corresponds to 4% of the population. Adjustment for apolipoprotein(a) isoforms further modified the effect estimates markedly. ConclusionsThis work emphasizes the complexity of the genetic regulation of Lp(a) and the importance to account for genetic subgroups in Lp(a) association studies and when interpreting genetic cardiovascular risk profiles.