{"doi":"10.1002/ajh.26570","title":"Validation of single‐gene noninvasive prenatal testing for sickle cell disease","abstract":"Screening all pregnancies for sickle cell disease (SCD), as recommended by the American College of Gynecologists,1 is essential to enable informed decisions about diagnostic testing, clinical care, and expand available gene therapy treatment options.2-4 However, traditional prenatal screening, in which both maternal and paternal DNA are required for carrier screening, has a low sensitivity of 42% due to insufficient paternal screening uptake in the United States.5 Even when paternal screening is performed and the carrier result is positive, the maximum fetal disease risk is one in four. Single-gene noninvasive prenatal testing (sgNIPT) is a promising new technology, able to achieve accurate prenatal results without requiring paternal screening. We previously reported the proof-of-principle development of a sequencing-based sgNIPT test for five conditions, including SCD, in 2019.6 Single-gene NIPT analyzes cell-free DNA (cfDNA) from maternal plasma to provide a personalized fetal residual disease risk ranging from >9 in 10 to <1 in 20,000. The aim of this study was to build upon our previous work to validate the sgNIPT in clinical samples and identify high-risk SCD fetuses in a cohort of at-risk pregnancies. This retrospective clinical study collected 77 maternal blood samples between October 2018 and December 2019 from pregnant patients at the Baylor College of Medicine or the University of Alabama at Birmingham who were known to have at least one pathogenic HBB allele. Newborn HBB genotype was determined by newborn screening chart review or genotyping of umbilical cord blood. For sgNIPT processing, genomic DNA (gDNA) and cfDNA were extracted from the maternal blood sample. The SCD maternal carrier status was determined by next-generation sequencing of the gDNA. The cfDNA fraction was then sequenced to determine (1) fetal fraction, (2) molecular counts of cfDNA, (3) maternal variant fraction, and (4) variants that are not present in the maternal genotype (paternally inherited variants).6 All patients start with an a priori risk calculated from the pregnant patient's carrier status, the highest subpopulation HBB carrier frequency in the United States (one in eight),7 and the likelihood a fetus inherits two SCD alleles. A likelihood ratio is calculated through relative dosage analysis of the most abundant allele found in cfDNA, comparing the likelihood of inheriting one copy or two copies of the most abundant allele.6 The likelihood ratio was used (1) to calculate the genotype score and predict fetal HBB genotype (Figure 1A) and (2) to adjust the a priori risk and calculate a residual risk that the fetus is affected with SCD (Figure 1B). In a clinical report, the residual and associated risk categories (low risk, decreased risk, or high risk) would be provided. Out of the 77 pregnancies in the cohort, maternal HBB genotypes included HbAS (n = 59), HbAC (n = 13), HbSS (n = 4), and HbCC (n = 1). The median fetal fraction was 9.3% (IQR = 5.8%–13.6%) for gestational ages ranging from 16.4 weeks to collection at delivery (Table S1). The fetal fraction was similar to a large clinical study that reported a median fetal fraction of 10.0% (IQR = 7.8%–13.0%) for gestational ages 11–13 weeks.8 Therefore, this cohort is likely representative of the expected fetal fraction in the first trimester, when prenatal screening is most common. Single-gene NIPT returned a fetal HBB genotype prediction for 68 of the 77 pregnancies, with 9 undetermined. sgNIPT accurately distinguished heterozygous from homozygous fetuses (Figure 1A) with 100% sensitivity (90.8%–100%, 95% CI) and 96.5% specificity (82.2%–99.9%, 95% CI). The fetal genotype predictions were concordant with newborn genotypes in 67 out of 68 pregnancies (98.5%) (Tables S1 and 1). In the single discordant result, sgNIPT returned a fetal genotype prediction of HbAS when the newborn genotype was HbAA, both of which lead to low-risk results for sickle cell disease. To calculate the residual ris","journal":"American Journal of Hematology","year":2022,"id":257373,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9563,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":821659,"name":"David Tsao","orcid":"0000-0002-8096-8454","position":1,"is_corresponding":false},{"id":821660,"name":"Oguzhan Atay","orcid":"0000-0002-7203-9575","position":2,"is_corresponding":false},{"id":821661,"name":"Brian P. Landry","orcid":"0000-0001-6570-6950","position":3,"is_corresponding":false},{"id":907333,"name":"Patrick Ye","orcid":"0000-0003-1160-5016","position":4,"is_corresponding":false},{"id":451162,"name":"Devon Chandler‐Brown","orcid":"0000-0001-8454-7318","position":5,"is_corresponding":false},{"id":561007,"name":"Brian Alford","orcid":"0000-0002-3064-1939","position":6,"is_corresponding":false},{"id":821662,"name":"Jennifer Hoskovec","orcid":"0000-0002-0637-0120","position":7,"is_corresponding":false},{"id":420638,"name":"Akila Subramaniam","orcid":"0000-0003-0753-805X","position":8,"is_corresponding":false},{"id":822093,"name":"Kevin M. Pawlik","orcid":null,"position":9,"is_corresponding":false},{"id":821663,"name":"Spencer G. Kuper","orcid":"0000-0003-0854-1138","position":10,"is_corresponding":false},{"id":263440,"name":"Frederick D. Goldman","orcid":"0000-0002-0764-4883","position":11,"is_corresponding":false},{"id":695901,"name":"Tim M. Townes","orcid":null,"position":12,"is_corresponding":false},{"id":24878,"name":"Vivien Sheehan","orcid":"0000-0003-2837-2255","position":13,"is_corresponding":false},{"id":463604,"name":"Erik Westin","orcid":"0000-0002-5771-8457","position":0,"is_corresponding":true}],"reference_count":7,"raw_metadata":null,"created_at":"2026-07-19T00:25:30.183987Z","pmid":"35429177","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}