The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection

Abstract

BackgroundSequence information generated from next generation sequencing is often computationally phased using haplotype-phasing algorithms. Utilizing experimentally derived allele or haplotype information improves this prediction, as routinely used in HLA typing. We recently established a large dataset of long ERMAP alleles, which code for protein variants in the Scianna blood group system. We propose the phylogeny of this set of 48 alleles and identify evolutionary steps to derive the observed alleles.MethodsThe nucleotide sequence of > 21 kb each was used for all physically confirmed 48 ERMAP alleles that we previously published. Full-length sequences were aligned and variant sites were extracted manually. The Bayesian coalescent algorithm implemented in BEAST v1.8.3 was used to estimate a coalescent phylogeny for these variants and the allelic ancestral states at the internal nodes of the phylogeny.ResultsThe phylogenetic analysis allowed us to identify the evolutionary relationships among the 48 ERMAP alleles, predict 4243 potential ancestral alleles and calculate a posterior probability for each of these unobserved alleles. Some of them coincide with observed alleles that are extant in the population.ConclusionsOur proposed strategy places known alleles in a phylogenetic framework, allowing us to describe as-yet-undiscovered alleles. In this new approach, which relies heavily on the accuracy of the alleles used for the phylogenetic analysis, an expanded set of predicted alleles can be used to infer alleles when large genotype data are analyzed, as typically generated by high-throughput sequencing. The alleles identified by studies like ours may be utilized in designing of microarray technologies, imputing of genotypes and mapping of next generation sequencing data.Electronic supplementary materialThe online version of this article (10.1186/s12967-019-1791-9) contains supplementary material, which is available to authorized users.

ACKR1 Alleles at 5.6 kb in a Well-Characterized Renewable US Food and Drug Administration (FDA) Reference Panel for Standardization of Blood Group Genotyping

“…30 Computerized allele prediction, reliable in predicting common alleles, is inadequate when rare alleles are encountered (Table 3). In addition to this advance in technology, the data drawn from our study can be useful to identify 12 additional ACKR1 alleles that have an effect on complex diseases such as benign ethnic neutropenia, 31 osteoporosis, 32 and various cancers. 33 An example of the clinical importance of a correct allele determination is the GATA box mutation in the ACKR1 gene.…”

“…10 Unlike HLA, 11 there is a lack of experimentally confirmed allele information for blood group genes, which is needed to improve the inference accuracy. 12 A database of full-length alleles of all blood group genes among the samples used by the FDA to develop reference panels will aid in the development, validation, and proficiency testing of new blood group genotyping assays using NGS. 12 The ACKR1 gene encodes a multipass transmembrane glycoprotein that carries the five antigens of the Duffy (Fy) blood group system.…”

“…12 A database of full-length alleles of all blood group genes among the samples used by the FDA to develop reference panels will aid in the development, validation, and proficiency testing of new blood group genotyping assays using NGS. 12 The ACKR1 gene encodes a multipass transmembrane glycoprotein that carries the five antigens of the Duffy (Fy) blood group system. 13 The two major and clinically most significant antithetical antigens, Fy a and Fy b , have been implicated in severe hemolytic transfusion reactions and hemolytic disease of the fetus and newborn.…”

SCAR: The high‐prevalence antigen 013.008 in the Scianna blood group system

“…ERMAP is a glycosylated protein highly expressed on erythroid tissues and weakly expressed on leukocytes, thymus, lymph nodes, spleen, and bone marrow cells of adults and in fetal liver 5 . It functions as a cell adhesion or receptor molecule and potentially as an immune regulator 6 …”

“…5 It functions as a cell adhesion or receptor molecule and potentially as an immune regulator. 6 We recently reviewed reports on allo-and autoantibodies to SC antigens. 3,7 While antibodies against SC antigens are infrequently found, some are clinically significant.…”