Publications by authors named "William Salerno"

Background/objective: Recurrent acute pancreatitis (RAP) and chronic pancreatitis (CP) lack effective therapies. There is no consensus or guidance on which endpoints or outcome measures should be used in clinical trials. This study aimed to develop a core outcome set aligned with both patient and provider priorities for RAP and CP.

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Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies.

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  • COVID-19 and influenza are respiratory illnesses caused by different viruses but share some symptoms and clinical risk factors, yet their genetic connections remain poorly understood.
  • A study involving over 18,000 influenza cases and nearly 276,000 control subjects found no common genetic risk factors between COVID-19 and influenza, revealing specific gene variants linked only to influenza.
  • The research highlights the potential for targeting cell surface receptors involved in viral entry, showing that manipulating specific genes could lead to treatments that prevent both COVID-19 and influenza infections.
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  • Researchers analyzed genetic data from nearly 1 million individuals to create a comprehensive catalogue of human protein-coding variations, shedding light on gene function and the frequency of rare coding variants.
  • The study identified over 10 million missense and 1.1 million loss-of-function variants, discovering 1,751 novel genes with rare biallelic loss-of-function variants and 3,988 genes intolerant to these variants.
  • They estimate that 3% of people carry a clinically significant genetic variant and provide public access to their data to enhance genetic interpretation and support precision medicine.
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  • The Mexico City Prospective Study is a large-scale research initiative involving over 150,000 adults from urban areas in Mexico City, aimed at understanding genetic diversity and ancestry.
  • The study reveals a mix of Indigenous American, European, and African ancestries among participants, highlighting significant genetic differences and a unique genetic landscape within the Indigenous Mexican population.
  • Researchers created a valuable reference panel for genetic research, improving the accuracy of studying genetic variants in populations with high Indigenous ancestry, and providing essential resources for future genetic studies in both Mexico and the US.
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Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants.

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  • Researchers compiled a comprehensive catalog of 355,667 structural variants (SVs) from DNA data, with over half being novel, to better understand the relationship between SVs and diseases.
  • The study involved rigorous methods to ensure high-quality variant identification, showing over 90% accuracy compared to previous genetic assemblies.
  • This catalog reveals significant connections between SVs and various health traits, identifying 690 specific regions that may influence medically relevant genes, providing a crucial resource for disease research.
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Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.

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Background: The growing volume and heterogeneity of next-generation sequencing (NGS) data complicate the further optimization of identifying DNA variation, especially considering that curated high-confidence variant call sets frequently used to validate these methods are generally developed from the analysis of comparatively small and homogeneous sample sets.

Findings: We have developed xAtlas, a single-sample variant caller for single-nucleotide variants (SNVs) and small insertions and deletions (indels) in NGS data. xAtlas features rapid runtimes, support for CRAM and gVCF file formats, and retraining capabilities.

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  • A genome-wide association study identified a genetic variant (rs190509934) that reduces ACE2 expression by 37% and lowers the risk of SARS-CoV-2 infection by 40%.
  • The study confirms six previously known genetic risk variants, with four linked to worse outcomes in COVID-19 infected individuals.
  • A risk score based on common variants was developed, which improves prediction of severe disease beyond just demographic and clinical factors.
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A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.

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The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery.

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential.

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  • A new approach in population genetics involves genotyping large cohorts with low-resolution techniques and then resequencing selected individuals using more comprehensive long-read sequencing to capture genetic diversity.
  • SVCollector is a tool that identifies an optimal subset of individuals for resequencing by analyzing population-level genetic data, focusing on inclusion across various subpopulations to ensure a representative sample.
  • By applying a combination of fast algorithms, SVCollector has been shown to produce more balanced selections of individuals from diverse backgrounds compared to traditional naive methods, resulting in a better representation of genetic variants and diversity.
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Background: Structural variants (SVs) are critical contributors to genetic diversity and genomic disease. To predict the phenotypic impact of SVs, there is a need for better estimates of both the occurrence and frequency of SVs, preferably from large, ethnically diverse cohorts. Thus, the current standard approach requires the use of short paired-end reads, which remain challenging to detect, especially at the scale of hundreds to thousands of samples.

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  • Variant Call Format (VCF) is commonly used for representing genetic information but becomes very large with extensive data from population studies.
  • Sparse Project VCF (spVCF) is introduced as a more efficient version of VCF, reducing file sizes by over 10 times while retaining essential information.
  • The spVCF format is compatible with existing VCF systems and has been validated using data from large whole-exome sequencing projects, like DiscovEHR and UK Biobank.
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The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%).

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A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes.

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  • A correction has been issued for the paper mentioned.
  • Readers can find this correction through a link provided at the top of the original paper.
  • It's important to review the correction for accurate information.
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Purpose: To provide a validated method to confidently identify exon-containing copy-number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.

Methods: DNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples, when the target log ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies.

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  • VCPA is a Variant Calling Pipeline and data management tool designed for analyzing whole genome and exome sequencing, specifically for the Alzheimer's Disease Sequencing Project.
  • It consists of a pipeline for aligning sequence reads and calling variants, and a tracking database for real-time job status and quality metrics visualization, optimized for use on the Amazon cloud.
  • VCPA is available for free under the MIT license, with source code and instructions accessible from the National Institute on Aging's website for academic and nonprofit use.
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