Publications by authors named "Bryan Howie"

TruAB Discovery is an approach that integrates cellular immunology, high-throughput immunosequencing, bioinformatics, and computational biology in order to discover naturally occurring human antibodies for prophylactic or therapeutic use. We adapted our previously described pairSEQ technology to pair B cell receptor heavy and light chains of SARS-CoV-2 spike protein-binding antibodies derived from enriched antigen-specific memory B cells and bulk antibody-secreting cells. We identified approximately 60,000 productive, in-frame, paired antibody sequences, from which 2,093 antibodies were selected for functional evaluation based on abundance, isotype and patterns of somatic hypermutation.

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The SARS-CoV-2 Omicron variant of concern (VoC) and its sublineages contain 31-36 mutations in spike and escape neutralization by most therapeutic antibodies. In a pseudovirus neutralization assay, 66 of the nearly 400 candidate therapeutics in the Coronavirus Immunotherapeutic Consortium (CoVIC) panel neutralize Omicron and multiple Omicron sublineages. Among natural immunoglobulin Gs (IgGs), especially those in the receptor-binding domain (RBD)-2 epitope community, nearly all Omicron neutralizers recognize spike bivalently, with both antigen-binding fragments (Fabs) simultaneously engaging adjacent RBDs on the same spike.

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Background: The clonoSEQ® Assay (Adaptive Biotechnologies Corporation, Seattle, USA) identifies and tracks unique disease-associated immunoglobulin (Ig) sequences by next-generation sequencing of IgH, IgK, and IgL rearrangements and IgH-BCL1/2 translocations in malignant B cells. Here, we describe studies to validate the analytical performance of the assay using patient samples and cell lines.

Methods: Sensitivity and specificity were established by defining the limit of detection (LoD), limit of quantitation (LoQ) and limit of blank (LoB) in genomic DNA (gDNA) from 66 patients with multiple myeloma (MM), acute lymphoblastic leukemia (ALL), or chronic lymphocytic leukemia (CLL), and three cell lines.

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Immunotherapy has clinical activity in certain virally associated cancers. However, the tumor antigens targeted in successful treatments remain poorly defined. We used a personalized immunogenomic approach to elucidate the global landscape of antitumor T cell responses in complete regression of human papillomavirus-associated metastatic cervical cancer after tumor-infiltrating adoptive T cell therapy.

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Adoptive transfer of T cells with engineered T-cell receptor (TCR) genes that target tumor-specific antigens can mediate cancer regression. Accumulating evidence suggests that the clinical success of many immunotherapies is mediated by T cells targeting mutated neoantigens unique to the patient. We hypothesized that the most frequent TCR clonotypes infiltrating the tumor were reactive against tumor antigens.

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Genetic variation harbors signatures of natural selection driven by selective pressures that are often unknown. Estimating the ages of selection signals may allow reconstructing the history of environmental changes that shaped human phenotypes and diseases. We have developed an approximate Bayesian computation (ABC) approach to estimate allele ages under a model of selection on new mutations and under demographic models appropriate for human populations.

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Article Synopsis
  • Using reference panels from whole-genome sequencing (WGS) enhances the SNP data in genome-wide arrays, making it a cost-effective method.
  • The UK10K Cohorts project sequenced 3,781 genomes at low depth to explore genetic variation, particularly focusing on rare alleles in the British population.
  • Improved imputation accuracy for rare variants is achieved through re-phasing WGS panels and combining data from multiple sources, including the 1000 Genomes Project, while introducing a new method that balances speed and accuracy.
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The T cell receptor (TCR) protein is a heterodimer composed of an α chain and a β chain. TCR genes undergo somatic DNA rearrangements to generate the diversity of T cell binding specificities needed for effective immunity. Recently, high-throughput immunosequencing methods have been developed to profile the TCR α (TCRA) and TCR β (TCRB) repertoires.

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Purpose: High-throughput sequencing (HTS) of immunoglobulin heavy-chain genes (IGH) in unselected clinical samples for minimal residual disease (MRD) in B lymphoblastic leukemia (B-ALL) has not been tested. As current MRD-detecting methods such as flow cytometry or patient-specific qPCR are complex or difficult to standardize in the clinical laboratory, sequencing may enhance clinical prognostication.

Experimental Design: We sequenced IGH in paired pretreatment and day 29 post-treatment samples using residual material from consecutive, unselected samples from the Children's Oncology Group AALL0932 trial to measure MRD as compared with flow cytometry.

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High-throughput sequencing technologies produce short sequence reads that can contain phase information if they span two or more heterozygote genotypes. This information is not routinely used by current methods that infer haplotypes from genotype data. We have extended the SHAPEIT2 method to use phase-informative sequencing reads to improve phasing accuracy.

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Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk and have been shown to have beneficial effects in a broad range of patients. However, statins are associated with an increased risk, albeit small, of clinical myopathy and type 2 diabetes. Despite evidence for substantial genetic influence on LDL concentrations, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy or toxicity, and have produced little information regarding mechanisms that modulate statin response.

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Short insertions and deletions (indels) are the second most abundant form of human genetic variation, but our understanding of their origins and functional effects lags behind that of other types of variants. Using population-scale sequencing, we have identified a high-quality set of 1.6 million indels from 179 individuals representing three diverse human populations.

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The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs.

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Genotype imputation is a statistical technique that is often used to increase the power and resolution of genetic association studies. Imputation methods work by using haplotype patterns in a reference panel to predict unobserved genotypes in a study dataset, and a number of approaches have been proposed for choosing subsets of reference haplotypes that will maximize accuracy in a given study population. These panel selection strategies become harder to apply and interpret as sequencing efforts like the 1000 Genomes Project produce larger and more diverse reference sets, which led us to develop an alternative framework.

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We hypothesize that imputation based on data from the 1000 Genomes Project can identify novel association signals on a genome-wide scale due to the dense marker map and the large number of haplotypes. To test the hypothesis, the Wellcome Trust Case Control Consortium (WTCCC) Phase I genotype data were imputed using 1000 genomes as reference (20100804 EUR), and seven case/control association studies were performed using imputed dosages. We observed two 'missed' disease-associated variants that were undetectable by the original WTCCC analysis, but were reported by later studies after the 2007 WTCCC publication.

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Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk.

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In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases. Genotype imputation has been used widely in the analysis of GWA studies to boost power, fine-map associations and facilitate the combination of results across studies using meta-analysis. This Review describes the details of several different statistical methods for imputing genotypes, illustrates and discusses the factors that influence imputation performance, and reviews methods that can be used to assess imputation performance and test association at imputed SNPs.

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Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets.

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We report a genome-wide association (GWA) study of severe malaria in The Gambia. The initial GWA scan included 2,500 children genotyped on the Affymetrix 500K GeneChip, and a replication study included 3,400 children. We used this to examine the performance of GWA methods in Africa.

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We carried out a genome-wide association study of schizophrenia (479 cases, 2,937 controls) and tested loci with P < 10(-5) in up to 16,726 additional subjects. Of 12 loci followed up, 3 had strong independent support (P < 5 x 10(-4)), and the overall pattern of replication was unlikely to occur by chance (P = 9 x 10(-8)). Meta-analysis provided strongest evidence for association around ZNF804A (P = 1.

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We have genotyped 14,436 nonsynonymous SNPs (nsSNPs) and 897 major histocompatibility complex (MHC) tag SNPs from 1,000 independent cases of ankylosing spondylitis (AS), autoimmune thyroid disease (AITD), multiple sclerosis (MS) and breast cancer (BC). Comparing these data against a common control dataset derived from 1,500 randomly selected healthy British individuals, we report initial association and independent replication in a North American sample of two new loci related to ankylosing spondylitis, ARTS1 and IL23R, and confirmation of the previously reported association of AITD with TSHR and FCRL3. These findings, enabled in part by increased statistical power resulting from the expansion of the control reference group to include individuals from the other disease groups, highlight notable new possibilities for autoimmune regulation and suggest that IL23R may be a common susceptibility factor for the major 'seronegative' diseases.

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Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes.

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Common genetic polymorphism may explain a portion of the heritable risk for common diseases, so considerable effort has been devoted to finding and typing common single-nucleotide polymorphisms (SNPs) in the human genome. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), suggesting that only a subset of all SNPs (known as tagging SNPs, or tagSNPs) need to be genotyped for disease association studies. Based on the genetic differences that exist among human populations, most tagSNP sets are defined in a single population and applied only in populations that are closely related.

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