A Novel Framework for Characterizing Genomic Haplotype Diversity in the Human Immunoglobulin Heavy Chain Locus.
Cell Line
Computational Biology
/ methods
Data Display
Datasets as Topic
Family
Gene Library
Genes, Immunoglobulin
Genetic Variation
Genomic Structural Variation
Genotyping Techniques
Haplotypes
/ genetics
Humans
Immunoglobulin Heavy Chains
/ genetics
Molecular Sequence Annotation
Polymorphism, Genetic
Sequence Alignment
Sequence Analysis, DNA
Sequence Homology, Nucleic Acid
User-Computer Interface
Workflow
B cell receptor
antibody
immunoglobulin heavy chain locus
long-read sequencing
single nucleotide variation
structural variation
Journal
Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960
Informations de publication
Date de publication:
2020
2020
Historique:
received:
10
06
2020
accepted:
06
08
2020
entrez:
19
10
2020
pubmed:
20
10
2020
medline:
7
5
2021
Statut:
epublish
Résumé
An incomplete ascertainment of genetic variation within the highly polymorphic immunoglobulin heavy chain locus (IGH) has hindered our ability to define genetic factors that influence antibody-mediated processes. Due to locus complexity, standard high-throughput approaches have failed to accurately and comprehensively capture IGH polymorphism. As a result, the locus has only been fully characterized two times, severely limiting our knowledge of human IGH diversity. Here, we combine targeted long-read sequencing with a novel bioinformatics tool, IGenotyper, to fully characterize IGH variation in a haplotype-specific manner. We apply this approach to eight human samples, including a haploid cell line and two mother-father-child trios, and demonstrate the ability to generate high-quality assemblies (>98% complete and >99% accurate), genotypes, and gene annotations, identifying 2 novel structural variants and 15 novel IGH alleles. We show multiplexing allows for scaling of the approach without impacting data quality, and that our genotype call sets are more accurate than short-read (>35% increase in true positives and >97% decrease in false-positives) and array/imputation-based datasets. This framework establishes a desperately needed foundation for leveraging IG genomic data to study population-level variation in antibody-mediated immunity, critical for bettering our understanding of disease risk, and responses to vaccines and therapeutics.
Identifiants
pubmed: 33072076
doi: 10.3389/fimmu.2020.02136
pmc: PMC7539625
doi:
Substances chimiques
Immunoglobulin Heavy Chains
0
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2136Subventions
Organisme : NIAID NIH HHS
ID : R21 AI142590
Pays : United States
Organisme : Howard Hughes Medical Institute
Pays : United States
Organisme : NIAID NIH HHS
ID : R21 AI117407
Pays : United States
Organisme : NINDS NIH HHS
ID : F31 NS108797
Pays : United States
Organisme : NHGRI NIH HHS
ID : R25 HG007153
Pays : United States
Organisme : British Heart Foundation
ID : PG/14/26/30509
Pays : United Kingdom
Organisme : NIAID NIH HHS
ID : R01 AI121285
Pays : United States
Organisme : Medical Research Council
ID : G1100449
Pays : United Kingdom
Organisme : NIAID NIH HHS
ID : R24 AI138963
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG010169
Pays : United States
Organisme : NIH HHS
ID : S10 OD018522
Pays : United States
Organisme : NIH HHS
ID : S10 OD026880
Pays : United States
Informations de copyright
Copyright © 2020 Rodriguez, Gibson, Parks, Emery, Powell, Strahl, Deikus, Auckland, Eichler, Marasco, Sebra, Sharp, Smith, Bashir and Watson.
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