Evidence on genetic determinants of Hb F and quantified effects of key variants through meta-analysis is synthesized, supporting genotype-guided therapeutic, and genome-editing strategies.
Abstract
Fetal hemoglobin (Hb F) is the strongest endogenous modifier of sickle cell disease (SCD) severity, but its genetic regulation varies across populations. This review synthesized evidence on genetic determinants of Hb F and quantified effects of key variants through meta-analysis. Following PRISMA 2020 guidelines (PROSPERO: CRD420251042025), MEDLINE, EMBASE, Scopus, and Web of Science were searched through May 2026. Studies evaluating genetic associations with Hb F levels in SCD were included. Narrative synthesis and random-effects meta-analysis were performed. Eighty-four studies identified 80 variants across 32 genes associated with Hb F levels. The most consistently replicated associations involved BCL11A (rs1427407, rs4671393, and rs11886868), the HBS1L-MYB intergenic region (rs4895441, rs28384513), HBG2 (rs7482144), and HMOX1 (rs2071746). Meta-analysis confirmed directionally consistent associations for seven of eight variants. Additional modifiers included SIN3A, ZBTB7A, ANTXR1, FOXO3, BACH2, and HIF-1α. Hb F regulation in SCD is polygenic and influenced by both canonical and secondary modifiers, supporting genotype-guided therapeutic, and genome-editing strategies.
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Variants of uncertain significance (VUS) in the LMNA gene represent a major challenge in clinical genetics, as insufficient functional evidence limits their interpretation and clinical decision-making in laminopathies, including dilated cardiomyopathy (DCM). Here, we generated two isogenic induced pluripotent stem cell (iPSC) lines carrying homozygous LMNA variants, c.293A > G (p.Glu98Gly) and c.439G > A (p.Ala147Thr) by prime editing of a healthy donor iPSC line. Both variants are located within Coil 1B domain of lamin A. The edited iPSC lines retain normal morphology, pluripotency, genomic integrity, and trilineage differentiation capacity, providing a valuable platform for functional characterization and potential clinical reclassification of LMNA VUS.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026