Sickle cell disease (SCD) results from a genetic mutation in the beta hemoglobin gene. Despite the same underlying causal mutation, patients
exhibit highly heterogeneous clinical outcomes. Acute chest syndrome (ACS) is the leading cause of mortality among individuals with SCD,
accounting for 25% of all deaths, and is the second most common reason for hospitalizations after vaso-occlusive pain. It is associated with
extended hospital stays, higher risk of respiratory failure, and the potential for developing chronic lung disease after recurrent episodes. Many previous genome-wide association studies (GWAS) have failed to identify risk loci associated with ACS due to its multifactorial etiology and the absence of a well-defined, consensus phenotype.

To identify genetic loci associated with ACS, we performed a GWAS with 196 adult SCD patients from the Outcome Modifying Genes in SCD
(OMG-SCD) cohort. We selected the extreme phenotype, whereby patients who had never had any episode of ACS were categorized as controls
(n=114), while patients who had had more than 5 episodes of ACS were categorized as cases (n=82). All patients included in the study either
had HbSS (homozygous sickle) or HbSβ0 (sickle beta-0 thalassemia) genotype. Genotype data were generated by whole genome sequencing by
Trans-Omics for Precision Medicine - TOPMed (phs001608). We performed the GWAS using the R package SAIGE, incorporating treatment
(hydroxyurea) status and 2 genomic principal components as covariates. Linkage-disequilibrium clumping was performed using PLINK 1.9 to
identify lead single nucleotide polymorphisms (SNPs).

Our top SNP associated with ACS was rs7319269 on chromosome 13, reaching the genome-wide threshold of 5x10-8. This intergenic SNP lies
between SNORA107 and LINC00375. In total, 175 SNPs with p-value less than 5x10-5 were nominally associated with ACS. After adjusting for
False Discovery Rate (FDR), all these SNPs had q-values less than 0.05. Interestingly, one of our top SNPs (rs1356744 on chr 2) has been
previously associated with severe acute respiratory syndrome in COVID-19, suggesting a common biological pathway in ACS and COVID-19.
Our study highlights the importance of a well-defined phenotype with distinct cases and controls for successful identification of risk loci through GWAS. To further understand the biological implications of these SNPs, we are deploying post-GWAS methods for performing functional
analyses. Our goal is to decipher the intricate biological mechanisms and pathways involved in severe SCD so that we can enhance treatment
approaches and provide valuable insights into the molecular profiles specific to severe SCD.

Sickle cell disease (SCD) affects approximately 100,000 predominantly African-American individuals in the United States, causing significant cellular damage, increased disease complications, and premature death. The contribution of epigenetic factors to SCD pathophysiology is relatively unexplored. DNA methylation (DNAm), a primary epigenetic mechanism for regulating gene expression in response to the environment, is an important driver of normal cellular aging. Several DNAm epigenetic clocks have been developed to serve as a proxy for cellular aging. We calculated the epigenetic ages of 89 adults with SCD (mean age: 30.64 years; 60.64% female) using five published epigenetic clocks: Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. We hypothesized that in a chronic disease like SCD, individuals would demonstrate epigenetic age acceleration, but results differed depending on the clock used. Recently developed clocks more consistently demonstrated acceleration (GrimAge, DunedinPACE). Additional demographic and clinical phenotypes were analyzed to determine their associations with epigenetic age estimates. Chronological age was significantly correlated with epigenetic age in all clocks (Horvath, r = 0.88; Hannum, r = 0.89; PhenoAge, r = 0.85; GrimAge, r = 0.88; DunedinPACE, r=0.34). SCD genotype was associated with two clocks (PhenoAge, p = 0.02; DunedinPACE, p < 0.001). Genetic ancestry, biological sex, beta-globin haplotypes, BCL11A rs11886868 and SCD disease severity were not associated. These findings, among the first to interrogate epigenetic aging in adults with SCD, demonstrate epigenetic age acceleration with recently developed epigenetic clocks but not older generation clocks. Further development of epigenetic clocks may improve predictive ability and utility in chronic diseases like SCD.

Sickle cell disease (SCD) is a severe inherited blood disorder affecting about 100K US individuals, and blood transfusion is a key component in the clinical management of SCD patients. However, 8-76% SCD patients unfortunately become alloimmunized, many of which cause accelerated donor red blood cell destruction. Patients show substantial variability in their predisposition to alloimmunization, where genetic variability is one proposed component. Although several genetic association studies have been conducted for alloimmunization, the results have been inconsistent, and the genetic determinants of alloimmunization remain largely unknown. In an effort to advance genetic studies of alloimmunization, we performed a genome-wide association study (GWAS) in 236 African American (AA) SCD patients from the Duke OMG-SCD cohort, which is part of TOPMed, with whole genome sequencing data available. We applied logistic mixed models adjusting for sample relationship matrix and covariates, including presence of autoantibodies, hemoglobin genotype categories and sex. Despite the small sample size, we identified one genome-wide significant locus on chr12 (p = 3.1e-9) with no evidence of genomic inflation (lambda = 1.003). We also performed sensitivity analyses adjusting for additional covariates (Fyb antigen and the amount of blood transfusion) and applying different sample grouping strategies based on the number of alloantibodies patients developed (>=2 v.s. 0 and >=2 v.s. <=1). These analyses consistently revealed several additional suggestive loci. Furthermore, these suggestive loci are supported by both eQTL evidence from GTEx whole blood and/or Jackson Heart Study PBMC RNA-seq data, and 3D chromatin conformation information derived from HiC data in spleen and/or K562 cells. In conclusion, we identified several novel genetic variants that are significantly associated with alloimmunization among SCD patients and linked those variants to genes leveraging functional annotation information. We call for the community to collect additional alloantibody information within SCD cohorts to further the understanding of the genetic basis of alloimmunization in order to improve transfusion outcomes.

Introduction
Clinical trials for using hematopoietic stem cell transplant (HSCT) with gene therapies to cure sickle cell disease (SCD) began in 2019. The Food and Drug Administration (FDA) requires that 1) all gene therapy participants receive at least 15 years of ongoing surveillance and 2) their outcomes be compared to a contemporaneous cohort that did not receive gene therapy. Previously we developed an automated contemporaneous cohort of children and adults with SCD from electronic health record (EHR) data (Cronin RM et al. 2023, Blood Adv). The genotype underlying SCD can influence the severity of the disease presentation. Our work was built upon previously explored algorithms utilizing ICD codes and laboratory data to identify persons with sickle cell anemia (SCA), a severe form of SCD (Singh et al. 2018, Blood Adv). Still, our algorithm additionally classifies other predicted genotypes. However, we could not test the SCD phenotypes against the SCD genotypes due to the absence of beta-globin gene sequence data. Here we tested the hypothesis that our Vanderbilt-EHR algorithm for identifying a contemporaneous cohort at Vanderbilt can classify their predicted genotype compared to beta-globin gene sequencing, the gold standard for SCD diagnosis, in the cohort. We subsequently tested the Vanderbilt-EHR algorithm in an established cohort of children from St. Jude Children’s Research Hospital that have been deeply phenotyped.

Methods
We applied the Vanderbilt-EHR algorithm to a cohort of 275 samples from Vanderbilt's biorepository of DNA, BioVU, linked to de-identified medical records in a data warehouse called the Synthetic Derivative. These 275 samples were previously submitted for whole genome sequencing as part of the Genetic Variation of Heart, Lung, and Kidney Disease in Sickle Cell Disease: Pre- and Post-Curative Therapies TOPMed project (HLK-SCD phs002617), which provided back genotyped information for these samples. The genotypes were validated based on the known variants described in the hemoglobin variant database HbVar (Giardine BM et al. 2021, Nucleic Acids Res) (Table 1). We applied only the hemoglobinopathy genotype classification portion of the algorithm to the Sickle Cell Clinical Research and Intervention Program (SCCRIP) cohort from St. Jude Children’s Research Hospital, which is a deeply phenotyped registry of persons with SCD that includes putative genotype diagnoses derived from a comprehensive evaluation of clinical data for each member. We performed statistical analyses of the identification and classification results of the Vanderbilt cohort and the SCCRIP cohort's classification results.

Results
Of the 275 genotyped samples, the algorithm correctly predicted 255 SCD cases and 10 non-SCD cases. There were three cases predicted to be false positives; however, a comprehensive analysis showed that all have laboratory values indicative of SCA (high hemoglobin S levels >60%, absent or negligible hemoglobin A levels, elevated reticulocytes, and low mean corpuscular volume) suggesting that there may be a genotyping error. The remaining 7 cases were either indeterminate in the prediction or in the genotype (Table 1). Given the small number of indeterminate results, we performed a sensitivity analysis to identify the range of performance for SCD identification. The method performed very well with over 98% sensitivity and over 96% positive predictive value (PPV) (Table 2). The lower specificity and negative predictive value (NPV) could be largely attributed to the disproportionately high number of SCD cases in the dataset compared to non-SCD cases. The SCD classification algorithm performed similarly in both the VUMC and SCCRIP cohorts (Table 2). The algorithm performs best in identifying SC and SCA types of SCD with nearly 100% accuracy for SC and over 94% accuracy for SCA. Transfusions can confound the diagnosis of SCA vs. S beta thalassemia +, but the latter has a much lower prevalence consistent with our results (Table 2).

Discussion and Conclusions
Larger SCD cohorts will be identified using these approaches in singular EHR databases and in de-identified data warehouses that EHR companies have developed by aggregating data across multiple EHR systems (e.g., EPIC Cosmos and Cerner Real-World Data). The ongoing need for contemporaneous comparison cohorts of persons with SCD to understand outcomes related to treatment and phenotype can be greatly assisted by accurate automated approaches. The Vanderbilt-EHR algorithm is the first to comprehensively phenotype SCD and other hemoglobinopathies involving variant hemoglobin beta alleles.

Sickle cell disease (SCD) is a severe inherited blood disorder affecting about 100K US individuals, and blood transfusion is a key component in the clinical management of SCD patients. However, 8-76% SCD patients unfortunately become alloimmunized, many of which cause accelerated donor red blood cell destruction. Patients show substantial variability in their predisposition to alloimmunization, where genetic variability is one proposed component. Although several genetic association studies have been conducted for alloimmunization, the results have been inconsistent, and the genetic determinants of alloimmunization remain largely unknown. In an effort to advance genetic studies of alloimmunization, we performed a genome-wide association study (GWAS) in 236 African American (AA) SCD patients from the Duke OMG-SCD cohort, which is part of TOPMed, with whole genome sequencing data available. We applied logistic mixed models adjusting for sample relationship matrix and covariates, including presence of autoantibodies, hemoglobin genotype categories and sex. Despite the small sample size, we identified one genome-wide significant locus on chr12 (p = 3.1e-9) with no evidence of genomic inflation (lambda = 1.003). We also performed sensitivity analyses adjusting for additional covariates (Fyb antigen and the amount of blood transfusion) and applying different sample grouping strategies based on the number of alloantibodies patients developed (>=2 v.s. 0 and >=2 v.s. <=1). These analyses consistently revealed several additional suggestive loci. Furthermore, these suggestive loci are supported by both eQTL evidence from GTEx whole blood and/or Jackson Heart Study PBMC RNA-seq data, and 3D chromatin conformation information derived from HiC data in spleen and/or K562 cells. In conclusion, we identified several novel genetic variants that are significantly associated with alloimmunization among SCD patients and linked those variants to genes leveraging functional annotation information. We call for the community to collect additional alloantibody information within SCD cohorts to further the understanding of the genetic basis of alloimmunization in order to improve transfusion outcomes.