Patterns of mosaicism for sequence and copy-number variants discovered through clinical deep sequencing of disease-related genes in one million individuals.

DNA variants that arise after conception can show mosaicism, varying in presence and extent among tissues. Mosaic variants have been reported in Mendelian diseases, but further investigation is necessary to broadly understand their incidence, transmission, and clinical impact. A mosaic pathogenic variant in a disease-related gene may cause an atypical phenotype in terms of severity, clinical features, or timing of disease onset. Using high-depth sequencing, we studied results from one million unrelated individuals referred for genetic testing for almost 1,900 disease-related genes. We observed 5,939 mosaic sequence or intragenic copy number variants distributed across 509 genes in nearly 5,700 individuals, constituting approximately 2% of molecular diagnoses in the cohort. Cancer-related genes had the most mosaic variants and showed age-specific enrichment, in part reflecting clonal hematopoiesis in older individuals. We also observed many mosaic variants in genes related to early-onset conditions. Additional mosaic variants were observed in genes analyzed for reproductive carrier screening or associated with dominant disorders with low penetrance, posing challenges for interpreting their clinical significance. When we controlled for the potential involvement of clonal hematopoiesis, most mosaic variants were enriched in younger individuals and were present at higher levels than in older individuals. Furthermore, individuals with mosaicism showed later disease onset or milder phenotypes than individuals with non-mosaic variants in the same genes. Collectively, the large compendium of variants, disease correlations, and age-specific results identified in this study expand our understanding of the implications of mosaic DNA variation for diagnosis and genetic counseling.

Identifying risk variants for embryo aneuploidy using ultra-low coverage whole-genome sequencing from preimplantation genetic testing.

Aneuploidy frequently arises during human meiosis and is the primary cause of early miscarriage and in vitro fertilization (IVF) failure. Individuals undergoing IVF exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using next-generation sequencing is a standard test for identifying and selecting IVF-derived euploid embryos. The wealth of embryo aneuploidy data and ultra-low coverage whole-genome sequencing (ulc-WGS) data from PGT-A have the potential to discover variants in parental genomes that are associated with aneuploidy risk in their embryos. Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in embryo genomes. We then used the imputed variants and embryo aneuploidy calls to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system. We identified one locus on chromosome 3 that is significantly associated with meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice. Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that has potential to be leveraged for similar association studies that use ulc-WGS data.

PanDepth, an ultrafast and efficient genomic tool for coverage calculation.

Coverage quantification is required in many sequencing datasets within the field of genomics research. However, most existing tools fail to provide comprehensive statistical results and exhibit limited performance gains from multithreading. Here, we present PanDepth, an ultra-fast and efficient tool for calculating coverage and depth from sequencing alignments. PanDepth outperforms other tools in computation time and memory efficiency for both BAM and CRAM-format alignment files from sequencing data, regardless of read length. It employs chromosome parallel computation and optimized data structures, resulting in ultrafast computation speeds and memory efficiency. It accepts sorted or unsorted BAM and CRAM-format alignment files as well as GTF, GFF and BED-formatted interval files or a specific window size. When provided with a reference genome sequence and the option to enable GC content calculation, PanDepth includes GC content statistics, enhancing the accuracy and reliability of copy number variation analysis. Overall, PanDepth is a powerful tool that accelerates scientific discovery in genomics research.

The Landscape and Perspectives of the Human Gut Metaproteomics.

The human gut microbiome is closely associated with human health and diseases. Metaproteomics has emerged as a valuable tool for studying the functionality of the gut microbiome by analyzing the entire proteins present in microbial communities. Recent advancements in liquid chromatography and tandem mass spectrometry (LC-MS/MS) techniques have expanded the detection range of metaproteomics. However, the overall coverage of the proteome in metaproteomics is still limited. While metagenomics studies have revealed substantial microbial diversity and functional potential of the human gut microbiome, few studies have summarized and studied the human gut microbiome landscape revealed with metaproteomics. In this article, we present the current landscape of human gut metaproteomics studies by re-analyzing the identification results from 15 published studies. We quantified the limited proteome coverage in metaproteomics and revealed a high proportion of annotation coverage of metaproteomics-identified proteins. We conducted a preliminary comparison between the metaproteomics view and the metagenomics view of the human gut microbiome, identifying key areas of consistency and divergence. Based on the current landscape of human gut metaproteomics, we discuss the feasibility of using metaproteomics to study functionally unknown proteins and propose a whole workflow peptide-centric analysis. Additionally, we suggest enhancing metaproteomics analysis by refining taxonomic classification and calculating confidence scores, as well as developing tools for analyzing the interaction between taxonomy and function.

The risk of losing health insurance in the United States is large, and remained so after the Affordable Care Act.

Health insurance coverage in the United States is highly uncertain. In the post-Affordable Care Act (ACA), pre-COVID United States, we estimate that while 12.5% of individuals under 65 are uninsured at a point in time, twice as many-one in four-are uninsured at some point over a 2-y period. Moreover, the risk of losing insurance remained virtually unchanged with the introduction of the landmark ACA. Risk of insurance loss is particularly high for those with health insurance through Medicaid or private exchanges; they have a 20% chance of losing coverage at some point over a 2-y period, compared to 8.5% for those with employer-provided coverage. Those who lose insurance can experience prolonged periods without coverage; about half are still uninsured 6 mo later, and almost one-quarter are uninsured for the subsequent 2 y. These facts suggest that research and policy attention should focus not only on the "headline number" of the share of the population uninsured at a point in time, but also on the stability and certainty (or lack thereof) of being insured.

Steering CO2 electroreduction pathway toward ethanol via surface-bounded hydroxyl species-induced noncovalent interaction.

Selective electroreduction of carbon dioxide (CO2RR) into ethanol at an industrially relevant current density is highly desired. However, it is challenging because the competing ethylene production pathway is generally more thermodynamically favored. Herein, we achieve a selective and productive ethanol production over a porous CuO catalyst that presents a high ethanol Faradaic efficiency (FE) of 44.1 ± 1.0% and an ethanol-to-ethylene ratio of 1.2 at a large ethanol partial current density of 501.0 ± 15.0 mA cm-2, in addition to an extraordinary FE of 90.6 ± 3.4% for multicarbon products. Intriguingly, we found a volcano-shaped relationship between ethanol selectivity and nanocavity size of porous CuO catalyst in the range of 0 to 20 nm. Mechanistic studies indicate that the increased coverage of surface-bounded hydroxyl species (*OH) associated with the nanocavity size-dependent confinement effect contributes to the remarkable ethanol selectivity, which preferentially favors the *CHCOH hydrogenation to *CHCHOH (ethanol pathway) via yielding the noncovalent interaction. Our findings provide insights in favoring the ethanol formation pathway, which paves the path toward rational design of ethanol-oriented catalysts.

SpliceWiz: interactive analysis and visualization of alternative splicing in R.

Alternative splicing (AS) is a crucial mechanism for regulating gene expression and isoform diversity in eukaryotes. However, the analysis and visualization of AS events from RNA sequencing data remains challenging. Most tools require a certain level of computer literacy and the available means of visualizing AS events, such as coverage and sashimi plots, have limitations and can be misleading. To address these issues, we present SpliceWiz, an R package with an interactive Shiny interface that allows easy and efficient AS analysis and visualization at scale. A novel normalization algorithm is implemented to aggregate splicing levels within sample groups, thereby allowing group differences in splicing levels to be accurately visualized. The tool also offers downstream gene ontology enrichment analysis, highlighting ASEs belonging to functional pathways of interest. SpliceWiz is optimized for speed and efficiency and introduces a new file format for coverage data storage that is more efficient than BigWig. Alignment files are processed orders of magnitude faster than other R-based AS analysis tools and on par with command-line tools. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization. SpliceWiz is a Bioconductor package and is also available on GitHub (https://github.com/alexchwong/SpliceWiz).

Structural coverage of the human interactome.

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein-protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein-protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome.

Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing.

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

Progress in Access and Oral Polio Vaccine Coverage Among Children Aged <5 Years in Polio Campaigns After the Political Change in Afghanistan.

BACKGROUND: Warfare has long impeded vaccination programs in polio-endemic Afghanistan. We aimed to describe progress in access to children under 5, oral polio vaccine (OPV) coverage among children under 5 in nationwide polio campaigns, and polio surveillance performance indicators after the Islamic Republic of Afghanistan collapsed to Taliban forces in August 2021. METHODS: Trends in the number of wild poliovirus type 1 (WPV1) and circulating vaccine-derived poliovirus type 2 (cVDPV2) cases and surveillance indicators from 2015 to 2023, and trends in the OPV coverage in the November 2020-June 2022 polio campaigns, were described. RESULTS: From 2015 to mid-July 2020, 74 of 126 (58.7%) WPV1 cases were reported from inaccessible areas. In November 2020, 34.1% of target children under 5 were inaccessible; in November 2021 (the first postchange polio campaign), all were accessible. From November 2020, under-5 OPV coverage of 69.9% rose steadily to 99.9% in the May 2022 campaign. The number of cVDPV cases fell from 308 (2020) to zero (2022). June 2022's house-to-house OPV coverage was 34.2% higher than non-house-to-house modalities. Nonpolio acute flaccid paralysis and stool adequacy rates rose from 18.5/100 000 and 92.6% in 2020 to 24.3/100 000 and 94.4% in 2022, respectively. CONCLUSIONS: Children's inaccessibility no longer vitiates polio eradication; polio surveillance systems are less likely to miss any poliovirus circulation.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.

BACKGROUND: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. RESULTS: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. CONCLUSION: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. AVAILABILITY: A python package is available at: https://github.com/SayehSobhani/AFITBin .

Determining Herd Immunity Thresholds for Hepatitis A Virus Transmission to Inform Vaccination Strategies Among People Who Inject Drugs in 16†US States.

BACKGROUND: Widespread outbreaks of person-to-person transmitted hepatitis A virus (HAV), particularly among people who inject drugs (PWID), continue across the United States and globally. However, the herd immunity threshold and vaccination coverage required to prevent outbreaks are unknown. We used surveillance data and dynamic modeling to estimate herd immunity thresholds among PWID in 16 US states. METHODS: We used a previously published dynamic model of HAV transmission calibrated to surveillance data from outbreaks involving PWID in 16 states. Using state-level calibrated models, we estimated the basic reproduction number (R0) and herd immunity threshold for PWID in each state. We performed a meta-analysis of herd immunity thresholds to determine the critical vaccination coverage required to prevent most HAV outbreaks among PWID. RESULTS: Estimates of R0 for HAV infection ranged from 2.2 (95% confidence interval [CI], 1.9-2.5) for North Carolina to 5.0 (95% CI, 4.5-5.6) for West Virginia. Corresponding herd immunity thresholds ranged from 55% (95% CI, 47%-61%) for North Carolina to 80% (95% CI, 78%-82%) for West Virginia. Based on the meta-analysis, we estimated a pooled herd immunity threshold of 64% (95% CI, 61%-68%; 90% prediction interval, 52%-76%) among PWID. Using the prediction interval upper bound (76%) and assuming 95% vaccine efficacy, we estimated that vaccination coverage of 80% could prevent most HAV outbreaks. CONCLUSIONS: Hepatitis A vaccination programs in the United States may need to achieve vaccination coverage of at least 80% among PWID in order to prevent most HAV outbreaks among this population.

Coverage of Two-Dose Preemptive Cholera Mass Vaccination Campaign in High-Priority Hotspots in Shashemene, Oromia Region, Ethiopia.

BACKGROUND: Cholera is a public health priority in Ethiopia. The Ethiopian National Cholera Plan elaborates a multi-year scheme of oral cholera vaccine (OCV) use. Aligned with this, a preemptive OCV campaign was conducted under our Ethiopia Cholera Control and Prevention project. Here, we present the OCV vaccination outcomes. METHOD: Cholera high-priority hotspots in the Oromia Region, Shashemene Town (ST) and Shashemene Woreda (SW), were selected. Four kebelles (Abosto, Alelu, Arada, and Awasho) in ST and 4 clusters (Faji Gole, Harabate, Toga, and Chabi) in SW were study sites with OCV areas nested within. A total of 40 000 and 60 000 people in ST and SW, respectively, were targeted for a 2-dose OCV (Euvichol-Plus) campaign in 11-15 May (first round [R1]) and 27-31 May (second round [R2]) 2022. Daily administrative OCV coverage and a coverage survey in 277 randomly selected households were conducted. RESULTS: The administrative OCV coverage was high: 102.0% for R1 and 100.5% for R2 in ST and 99.1% (R1) and 100.0% (R1) in SW. The coverage survey showed 78.0% (95% confidence interval [CI]: 73.1-82.9) of household members with 2-dose OCV and 16.8% (95% CI: 12.4-21.3) with no OCV in ST; and 83.1% (95% CI: 79.6-86.5) with 2-dose OCV and 11.8% (95% CI: 8.8-14.8) with no OCV in SW. The 2-dose coverages in 1-4-, 5-14-, and ≥15-year age groups were 88.3% (95% CI: 70.6-96.1), 88.9% (95% CI: 82.1-95.7), and 71.3% (95% CI: 64.2-78.3), respectively, in ST and 78.2% (95% CI: 68.8-87.7), 91.0% (95% CI: 86.6-95.3), and 78.7% (95% CI: 73.2-84.1) in SW. CONCLUSIONS: High 2-dose OCV coverage was achieved. Cholera surveillance is needed to assess the vaccine impact and effectiveness.

Insights into the genome of the 'Loco' Concholepas concholepas (Gastropoda: Muricidae) from low-coverage short-read sequencing: genome size, ploidy, transposable elements, nuclear RNA gene operon, mitochondrial genome, and phylogenetic placement in the family Muricidae.

BACKGROUND: The Peruvian 'chanque' or Chilean 'loco' Concholepas concholepas is an economically, ecologically, and culturally important muricid gastropod heavily exploited by artisanal fisheries in the temperate southeastern Pacific Ocean. In this study, we have profited from a set of bioinformatics tools to recover important biological information of C. concholepas from low-coverage short-read NGS datasets. Specifically, we calculated the size of the nuclear genome, ploidy, and estimated transposable elements content using an in silico k-mer approach, we discovered, annotated, and quantified those transposable elements, we assembled and annotated the 45S rDNA RNA operon and mitochondrial genome, and we confirmed the phylogenetic position of C. concholepas within the muricid subfamily Rapaninae based on translated protein coding genes. RESULTS: Using a k-mer approach, the haploid genome size estimated for the predicted diploid genome of C. concholepas varied between 1.83 Gbp (with kmer = 24) and 2.32 Gbp (with kmer = 36). Between half and two thirds of the nuclear genome of C. concholepas was composed of transposable elements. The most common transposable elements were classified as Long Interspersed Nuclear Elements and Short Interspersed Nuclear Elements, which were more abundant than DNA transposons, simple repeats, and Long Terminal Repeats. Less abundant repeat elements included Helitron mobile elements, 45S rRNA DNA, and Satellite DNA, among a few others.The 45S rRNA DNA operon of C. concholepas that encodes for the ssrRNA, 5.8S rRNA, and lsrRNA genes was assembled into a single contig 8,090 bp long. The assembled mitochondrial genome of C. concholepas is 15,449 bp long and encodes 13 protein coding genes, two ribosomal genes, and 22 transfer RNAs. CONCLUSION: The information gained by this study will inform the assembly of a high quality nuclear genome for C. concholepas and will support bioprospecting and biomonitoring using environmental DNA to advance development of conservation and management plans in this overexploited marine snail.

Access to colorectal cancer screening in populations in China, 2020: A coverage-focused synthesis analysis.

In populations in China, colorectal cancer (CRC) screening can be mainly accessed through organized screening, opportunistic screening, and physical examination. This screening intervention is found to be effective but the exact coverage rate is difficult to measure. Based on data from published articles, official websites, and available program reports, the screening coverage rate and related indicators were quantified. A rapid review was then conducted to estimate the overall and the breakdown coverage rates of the sub-type screening services, by leveraging the numbers of articles and the by-type median sample sizes. Up to 2020, two central government-funded and four provincial/municipal-level organized CRC screening programs have been initiated and included in this analysis. For populations aged 40-74, the estimated coverage rate of organized programs in China was 2.7% in 2020, and the 2-year cumulative coverage rate in 2019-2020 was 5.3% and the 3-year cumulative coverage rate in 2018-2020 was 7.7%. The corresponding coverage rates of 50-74-year-olds were estimated to be 3.4%, 7.1%, and 10.3%, respectively. Based on the rapid review approach, the overall screening coverage rate for 40-74 years, considering organized screening programs, opportunistic screening, and physical examinations, was then estimated to be 3.0% in China in 2020. However, comparing the findings of this study with the number of health check-ups reported in the local national health statistics yearbooks suggests that the number of CRC physical examinations may be underestimated in this study. The findings suggest that further efforts are needed to improve population access to CRC screening in China. Furthermore, evidence for access to opportunistic CRC screening and physical examination is limited, and more quantitative investigation is needed.

Navigating the economic challenges in childhood cancer control in low- and middle-income countries: Insights from the CC-BRIDGE tool and the global initiative for childhood cancer.

The increasing incidence of childhood cancer in low- and middle-income countries (LMICs) presents significant economic and logistical challenges, affecting health care provision and equitable treatment access. This editorial explores the economic barriers to pediatric oncology care in LMICs, highlighting resource scarcity, socioeconomic inequities, and health care complexities. It emphasizes the need for detailed cost analysis within health systems complicated by inadequate data and variable treatment protocols. Central to the discussion is the "Childhood Cancers Budgeting Rapidly to Incorporate Disadvantaged Groups for Equity (CC-BRIDGE) Tool" from the manuscript by Nancy Bolous et al., who proposed an innovative method to estimate the cost of integrating childhood cancer services into National Cancer Control Plans. This tool aligns with the World Health Organization's Global Initiative for Childhood Cancer to enhance survival rates and advocate for universal health coverage in pediatric oncology. The CC-BRIDGE tool's methodological rigor provides a structured framework for cost analysis. Yet, it is recognized as an initial step requiring further enhancements for comprehensive economic forecasting and societal cost assessments. In conclusion, the editorial highlights the tool's critical role in incorporating childhood cancer care into national strategies in LMICs, contributing to the broader fight against cancer and advocating for comprehensive, equitable health care. It signifies a vital stride toward addressing pediatric oncology's economic challenges and supporting universal health coverage for childhood cancer care.

What Has the Undiagnosed Diseases Network Taught Us About the Clinical Applications of Genomic Testing?

Genetic testing has undergone a revolution in the last decade, particularly with the advent of next-generation sequencing and its associated reductions in costs and increases in efficiencies. The Undiagnosed Diseases Network (UDN) has been a leader in the application of such genomic testing for rare disease diagnosis. This review discusses the current state of genomic testing performed within the UDN, with a focus on the strengths and limitations of whole-exome and whole-genome sequencing in clinical diagnostics and the importance of ongoing data reanalysis. The role of emerging technologies such as RNA and long-read sequencing to further improve diagnostic rates in the UDN is also described. This review concludes with a discussion of the challenges faced in insurance coverage of comprehensive genomic testing as well as the opportunities for a larger role of testing in clinical medicine.

Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations.

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole-genome sequencing data. We show that low-coverage sequencing at a depth of ≥4× captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1×) performed comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4× sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.

Commercial Versus Medicaid Insurance and Use of High-Priced Anticancer Treatments.

BACKGROUND: Because the markups on cancer drugs vary by payor, providers' financial incentive to use high-price drugs is differential according to each patient's insurance type. We evaluated the association between patient insurer (commercial vs Medicaid) and the use of high-priced cancer treatments. MATERIALS AND METHODS: We linked cancer registry, administrative claims, and demographic data for individuals diagnosed with cancer in North Carolina from 2004 to 2011, with either commercial or Medicaid insurance. We selected cancers with multiple FDA-approved, guideline-recommended chemotherapy options and large price differences between treatment options: advanced colorectal, lung, and head and neck cancer. The outcome was a receipt of a higher-priced option, and the exposure was insurer: commercial versus Medicaid. We estimated risk ratios (RRs) for the association between insurer and higher-priced treatment using log-binomial models with inverse probability of exposure weights. RESULTS: Of 812 patients, 209 (26%) had Medicaid. The unadjusted risk of receiving higher-priced treatment was 36% (215/603) for commercially insured and 27% (57/209) for Medicaid insured (RR: 1.31, 95% CI: 1.02-1.67). After adjustment for confounders the association was attenuated (RR: 1.15, 95% CI: 0.81-1.65). Exploratory subgroup analysis suggested that commercial insurance was associated with increased receipt of higher-priced treatment among patients treated by non-NCI-designated providers (RR: 1.53, 95% CI: 1.14-2.04). CONCLUSIONS: Individuals with Medicaid and commercial insurance received high-priced treatments in similar proportion, after accounting for differences in case mix. However, modification by provider characteristics suggests that insurance type may influence treatment selection for some patient groups. Further work is needed to determine the relationship between insurance status and newer, high-price drugs such as immune-oncology agents.