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Most humans harbor both CD177neg and CD177pos neutrophils but 1–10% of people are CD177null, placing them at risk for formation of anti-neutrophil antibodies that can cause transfusion-related acute lung injury and neonatal alloimmune neutropenia. By deep sequencing the CD177 locus, we catalogued CD177 single nucleotide variants and identified a novel stop codon in CD177null individuals arising from a single base substitution in exon 7. This is not a mutation in CD177 itself, rather the CD177null phenotype arises when exon 7 of CD177 is supplied entirely by the CD177 pseudogene (CD177P1), which appears to have resulted from allelic gene conversion. In CD177 expressing individuals the CD177 locus contains both CD177P1 and CD177 sequences. The proportion of CD177hi neutrophils in the blood is a heritable trait. Abundance of CD177hi neutrophils correlates with homozygosity for CD177 reference allele, while heterozygosity for ectopic CD177P1 gene conversion correlates with increased CD177neg neutrophils, in which both CD177P1 partially incorporated allele and paired intact CD177 allele are transcribed. Human neutrophil heterogeneity for CD177 expression arises by ectopic allelic conversion. Resolution of the genetic basis of CD177null phenotype identifies a method for screening for individuals at risk of CD177 isoimmunisation. [ABSTRACT FROM AUTHOR]

A diversity of tools is available for identification of variants from genome sequence data. Given the current complexity of incorporating external software into a genome analysis infrastructure, a tendency exists to rely on the results from a single tool alone. The quality of the output variant calls is highly variable however, depending on factors such as sequence library quality as well as the choice of short-read aligner, variant caller, and variant caller filtering strategy. Here we present a two-part study first using the high quality ‘genome in a bottle’ reference set to demonstrate the significant impact the choice of aligner, variant caller, and variant caller filtering strategy has on overall variant call quality and further how certain variant callers outperform others with increased sample contamination, an important consideration when analyzing sequenced cancer samples. This analysis confirms previous work showing that combining variant calls of multiple tools results in the best quality resultant variant set, for either specificity or sensitivity, depending on whether the intersection or union, of all variant calls is used respectively. Second, we analyze a melanoma cell line derived from a control lymphocyte sample to determine whether software choices affect the detection of clinically important melanoma risk-factor variants finding that only one of the three such variants is unanimously detected under all conditions. Finally, we describe a cogent strategy for implementing a clinical variant detection pipeline; a strategy that requires careful software selection, variant caller filtering optimizing, and combined variant calls in order to effectively minimize false negative variants. While implementing such features represents an increase in complexity and computation the results offer indisputable improvements in data quality. [ABSTRACT FROM AUTHOR]