Unrelated Stem Cell Donor HLA Match Likelihood in the US Registry Incorporating HLA-DPB1 Permissive Mismatching

Gragert, Loren; Spellman, Stephen R.; Shaw, Bronwen E.; Maiers, Martin

doi:10.1016/j.jtct.2022.12.027

Cited by 16 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ALD is particularly useful when investigating LD in highly polymorphic gene-systems, where each locus displays large and very different numbers of alleles in a population. These ALD measures, computed using PyPop, have been used in anthropological studies dissecting LD in human populations ( 18 , 19 ); studies of permissible mismatches in HLA donor registries ( 20 ); and studies of HLA haplotypes and amino acid motifs that predispose for disease ( 21 ). Additional publications, using different implementations of the ALD, include studies of the impact of anti-malarial drugs on parasite populations among individuals with complex infection status ( 22 , 23 ).…”

Section: Methods and Resultsmentioning

confidence: 99%

PyPop: a mature open-source software pipeline for population genomics

Lancaster,

Single,

Mack

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

Python for Population Genomics (PyPop) is a software package that processes genotype and allele data and performs large-scale population genetic analyses on highly polymorphic multi-locus genotype data. In particular, PyPop tests data conformity to Hardy-Weinberg equilibrium expectations, performs Ewens-Watterson tests for selection, estimates haplotype frequencies, measures linkage disequilibrium, and tests significance. Standardized means of performing these tests is key for contemporary studies of evolutionary biology and population genetics, and these tests are central to genetic studies of disease association as well. Here, we present PyPop 1.0.0, a new major release of the package, which implements new features using the more robust infrastructure of GitHub, and is distributed via the industry-standard Python Package Index. New features include implementation of the asymmetric linkage disequilibrium measures and, of particular interest to the immunogenetics research communities, support for modern nomenclature, including colon-delimited allele names, and improvements to meta-analysis features for aggregating outputs for multiple populations.Code available at: https://zenodo.org/records/10080668 and https://github.com/alexlancaster/pypop

show abstract

Section: Methods and Resultsmentioning

confidence: 99%

PyPop: a mature open-source software pipeline for population genomics

Lancaster,

Single,

Mack

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Future steps involve leveraging the 7-locus haplotype data ( HLA-A∼B∼C∼DRB1∼DRB3/4/5∼DQB1∼DPB1 ) generated by Gragert et al (2023) to include more than 8 million US donors in this extensible analysis. 36 …”

Section: Discussionmentioning

confidence: 99%

Assessment of HLA-DPB1 genetic variation using an HLA-DP tool and its implications in clinical transplantation

et al. 2023

Self Cite

View full text Add to dashboard Cite

HLA-DP is a classic transplantation antigen mediating alloreactivity through T-cell epitope (TCE) diversity and expression level. A current challenge is integrating these functional features into the prospective selection of candidate unrelated donors for transplantation. Genetically, HLA-DPB1 exon 2 defines permissive and non-permissive TCE groups, and exons 2 and 3 (in linkage with rs9277534) inform low- and high-expression allotypes. In this study, we analyze 356,272 exon 2 - exon 3 phased sequences from individuals across five self-identified race and ethnicity categories: White, Hispanic, Asian or Pacific Islander, Black or African American, and American Indian or Alaskan Native. This sequence dataset reveals the complex relationship between TCE and expression models and the importance of exon 3 sequence data. We also studied archived donor search lists for 2,545 patients who underwent transplantation from an HLA-11/12 unrelated donor mismatched for a single HLA-DPB1 allele. Depending on the order in which TCE and expression criteria are considered, some patients have different TCE- and expression-favorable donors. Additionally, this dataset reveals many expression-favorable alternatives exist in the search lists. To improve the selection of candidate donors, we provide, disseminate, and automate our findings through our multi-faceted tool called ExPAT (Expression of HLA-DPAssessment Tool), consisting of a public web application, Python package, and analysis pipeline.

show abstract

“…Donor and patient race and ethnicity data impact multiple aspects of the stem-cell or solid-organ donor registry operations, particularly the HLA matching process for Hematopoietic Cell Transplantation (HCT). To appreciate the impact of SIRE data on stem-cell registry operations, an understanding of HLA haplotype frequencies (Kollman et al, 2007;Gragert et al, 2013;Gragert et al, 2023) and related processes is necessary. For decades, donor/recipient HLA matching has been crucial for the success of solid organ and stem-cell transplantation (van Rood, 2000;Sheldon and Poulton, 2006;Mahdi, 2013;Dehn et al, 2019).…”

Section: The Importance Of Donor and Patient Sire Informationmentioning

confidence: 99%

“…Statistical imputation is the process of filling data gaps or resolving data ambiguities using a guided statistical approach that utilizes reference data and a maximum likelihood or other statistical approach. Imputation of HLA data (Madbouly et al, 2014) utilizes a maximum likelihood approach and reference data from population haplotype frequencies (Gragert et al, 2013;Gragert et al, 2023) generated via the Expectation Maximization (EM) algorithm (Single et al, 2002;Kollman et al, 2007). Reference population-specific datasets are important for the accuracy of imputation predictions since HLA is closely linked to the evolutionary history of human populations.…”

Section: The Importance Of Donor and Patient Sire Informationmentioning

confidence: 99%

“…Reference population-specific datasets are important for the accuracy of imputation predictions since HLA is closely linked to the evolutionary history of human populations. While an HLA allele may have different frequencies in different populations, it is the haplotypes that are mostly representative of each population (Gragert et al, 2013;Gragert et al, 2023). This is due to the linkage disequilibrium (LD) patterns and the relatively low recombination rate particular to the MHC region (Dawkins and Lloyd, 2019) which leads to the inheritance of HLA alleles in conserved haplotypes (Dawkins and Lloyd, 2019).…”

Section: The Importance Of Donor and Patient Sire Informationmentioning

confidence: 99%

See 1 more Smart Citation

Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant

Madbouly,

Bolon

2024

Front. Genet.

View full text Add to dashboard Cite

Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.

show abstract

Unrelated Stem Cell Donor HLA Match Likelihood in the US Registry Incorporating HLA-DPB1 Permissive Mismatching

Cited by 16 publications

References 32 publications

PyPop: a mature open-source software pipeline for population genomics

PyPop: a mature open-source software pipeline for population genomics

Assessment of HLA-DPB1 genetic variation using an HLA-DP tool and its implications in clinical transplantation

Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant

Contact Info

Product

Resources

About