Challenges associated with the identification of germline variants on myeloid malignancy genomic profiling-a Singaporean experience.

Genomic profiling to identify myeloid-malignancy-related gene mutations is routinely performed for patients with suspected or definite myeloid malignancies. The most common specimen types in our experience are peripheral blood and bone marrow aspirates. Although primarily intended to identify somatic mutations, not infrequently, potentially clinically significant germline variants are also identified. Confirmation of the germline status of these variants is typically performed by hair follicle or skin fibroblast testing. If the germline variant is classified as a pathogenic or likely pathogenic variant and occurs in a gene known to be associated with a disease relevant to the patient's phenotype (for example, the identification of a DDX41 pathogenic variant in an individual with acute myeloid leukemia), the management algorithm is typically quite straightforward. Challenging situations may occur such as when the germline variant is classified as a pathogenic or likely pathogenic variant and occurs in a gene not known to be associated with the patient's phenotype/presenting complaint. We have encountered several such challenging cases in which potentially clinically significant germline variants were identified on the initial genomic profiling of peripheral blood or bone marrow aspirate. In this article, we present these cases and discuss the genetic counseling and management approaches.

Human leukocyte antigen allele and haplotype frequencies in Singapore bone marrow donors and cord blood units.

We describe the allele and haplotype frequencies seen in a volunteer unrelated bone marrow donor registry, a public cord blood bank, and donor/recipient samples processed by the Health Sciences Authority (HSA) in Singapore. Historical human leukocyte antigen (HLA) typing reports were anonymized and combined. They were checked for HLA typing nomenclature discrepancies or ambiguities using the HLA-net UNIFORMATE tool, and for analysis, the validated data were subsequently separated into Chinese, Malay, Indian, and "Others," according to the race classification system used in Singapore. Individual ethnic allele and haplotype frequencies were calculated with the HLA-net GENE[RATE] pipeline using basic statistics. The Basic Statistics Tool of HLA-net was used to estimate haplotype frequency using an expectation maximization algorithm, given a set of multi-allelic data pairs for a given HLA locus. The outputs downloaded from the site comprised plain text files with haplotype frequency estimates, results of a global linkage disequilibrium test, and standardized residuals (stdres) corresponding to deviations from expected frequencies. HLA typing results from 59,186 individuals met the inclusion criteria, yielding 118,372 analyzable alleles. In our study population, the haplotype A*33:03-B*58:01-C*03:02-DRB1*03:01～DQB1*02:01:01G with a frequency of 4.91% was the most common. This haplotype was also the most common among Singaporean Chinese donors. Consistent with the predominant Chinese population, haplotypes with a frequency greater than 1% were also the most frequently observed haplotypes in the Singaporean population. In the Malay donor population, the most common haplotype was A*33:03～B*44:03～C*07:01:01G～ DRB1*07:01-DQB1*02:01:01G, with a frequency of 3.41%, whereas within the Indian donor population, the most common haplotype was A*01:01-B*57:01-C*06:02～DRB1*07:01-DQB1*03:03, with a frequency of 3.42%. Haplotype diversity and composition statistics within donor pools provide HLA background data required for the targeted recruitment of donors to support the hematopoietic stem cell donor requirements of the country. These data may be used in the future to devise donor recruitment strategies for optimizing the donor pool through targeted publicity and accruals.