Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks.

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.

A genome-wide association study of alloimmunization in the TOPMed OMG-SCD cohort identifies a locus on chromosome 12.

BACKGROUND: Red cell alloimmunization after exposure to donor red cells is a very common complication of transfusion for patients with sickle cell disease (SCD), resulting frequently in accelerated donor red blood cell destruction. Patients show substantial differences in their predisposition to alloimmunization, and 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. STUDY DESIGN AND METHODS: We performed a genome-wide association study (GWAS) in 236 African American (AA) SCD patients from the Outcome Modifying Genes in Sickle Cell Disease (OMG-SCD) cohort, which is part of Trans-Omics for Precision Medicine (TOPMed), with whole-genome sequencing data available. We also performed sensitivity analyses adjusting for different sets of covariates and applied different sample grouping strategies based on the number of alloantibodies patients developed. RESULTS: We identified one genome-wide significant locus on chr12 (p = 3.1e-9) with no evidence of genomic inflation (lambda = 1.003). Further leveraging QTL evidence from GTEx whole blood and/or Jackson Heart Study PBMC RNA-Seq data, we identified a number of potential genes, such as ARHGAP9, STAT6, and ATP23, that may be driving the association signal. We also discovered some suggestive loci using different analysis strategies. DISCUSSION: 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.

Epigenetic Aging Associations With Psychoneurological Symptoms and Social Functioning in Adults With Sickle Cell Disease.

Objective: Sickle cell disease (SCD), the most common inherited blood disorder in the United States, is associated with severe psychoneurological symptoms. While epigenetic age acceleration has been linked to psychoneurological symptom burden in other diseases, this connection is unexplored in SCD. This study aimed to assess the association between epigenetic age acceleration and psychoneurological symptom burden in SCD. Methods: In this cross-sectional study, emotional impact, pain impact, sleep impact, social functioning, and cognitive function were assessed in 87 adults living with SCD. DNA methylation data were generated from blood specimens and used to calculate epigenetic age using five clocks (Horvath, Hannum, PhenoAge, GrimAge, & DunedinPACE). Associations between epigenetic age acceleration and symptoms were assessed. Results: The sample (N = 87) had a mean (SD) chronologic age was 30.6 (8.1) years. Epigenetic age acceleration was associated with several symptom outcomes. GrimAge age acceleration (ß = -0.49, p = .03) and increased DunedinPACE (ß = -2.23, p = .004) were associated with worse emotional impact scores. PhenoAge (ß = -0.32, p = .04) and the GrimAge (ß = -0.48, p = .05) age acceleration were associated with worse pain impact scores. Increased DunedinPACE (ß = -2.07 p = .04) were associated with worse sleep impact scores. Increased DunedinPACE (ß = -2.87, p = .005) was associated with worse social functioning scores. We did not find associations between epigenetic age acceleration and cognitive function in this sample. Conclusion: Epigenetic age acceleration was associated with worse symptom experiences, suggesting the potential for epigenetic age acceleration as a biomarker to aid in risk stratification or targets for intervention to mitigate symptom burden in SCD.

Deployment-related toxic exposures are associated with worsening mental and physical health after military service: Results from a self-report screening of veterans deployed after 9/11.

Exposure to toxins-such as heavy metals and air pollution-can result in poor health and wellbeing. Recent scientific and media attention has highlighted negative health outcomes associated with toxic exposures for U.S. military personnel deployed overseas. Despite established health risks, less empirical work has examined whether deployment-related toxic exposures are associated with declines in mental and physical health after leaving military service, particularly among the most recent cohort of veterans deployed after September 11, 2001. Using data from 659 U.S. veterans in the VISN 6 MIRECC Post-Deployment Mental Health Study, we tested whether self-reported toxic exposures were associated with poorer mental and physical health. At baseline, veterans who reported more toxic exposures also reported more mental health, ß = 0.14, 95% CI [0.04, 0.23], p = 0.004, and physical health symptoms, ß = 0.21, 95% CI [0.11, 0.30], p < 0.001. Over the next ten years, veterans reporting more toxic exposures also had greater increases in mental health symptoms, ß = 0.23, 95% CI [0.15, 0.31], p < 0.001, physical health symptoms, ß = 0.22, 95% CI [0.14, 0.30], p < 0.001, and chronic disease diagnoses, ß = 0.15, 95% CI [0.07, 0.23], p < 0.001. These associations accounted for demographic and military covariates, including combat exposure. Our findings suggest that toxic exposures are associated with worsening mental and physical health after military service, and this recent cohort of veterans will have increased need for mental health and medical care as they age into midlife and older age.

Demographic characteristics and epigenetic biological aging among post-9/11 veterans: Associations of DunedinPACE with sex, race, and age.

Measures of epigenetic aging derived from DNA methylation (DNAm) have enabled the assessment of biological aging in new populations and cohorts. In the present study, we used an epigenetic measure of aging, DunedinPACE, to examine rates of aging across demographic groups in a sample of 2,309 United States military veterans from the VISN 6 MIRECC's Post-Deployment Mental Health Study. As assessed by DunedinPACE, female veterans were aging faster than male veterans (ß = 0.39, 95 % CI [0.29, 0.48], p < .001), non-Hispanic Black veterans were aging faster than non-Hispanic White veterans (ß = 0.58, 95 % CI [0.50, 0.66], p < .001), and older veterans were biologically aging faster than younger veterans (ß = 0.21, 95 % CI [0.18, 0.25], p < .001). In secondary analyses, these differences in rates of aging were not explained by a variety of biopsychosocial covariates. In addition, the percentage of European genetic admixture in non-Hispanic Black veterans was not associated with DunedinPACE. Our findings suggest that female and non-Hispanic Black veterans are at greater risk of accelerated aging among post-9/11 veterans. Interventions that slow aging might provide relatively greater benefit among veterans comprising these at-risk groups.

Genome-wide DNA methylation analysis of cannabis use disorder in a veteran cohort enriched for posttraumatic stress disorder.

Cannabis use has been increasing over the past decade, not only in the general US population, but particularly among military veterans. With this rise in use has come a concomitant increase in cannabis use disorder (CUD) among veterans. Here, we performed an epigenome-wide association study for lifetime CUD in an Iraq/Afghanistan era veteran cohort enriched for posttraumatic stress disorder (PTSD) comprising 2,310 total subjects (1,109 non-Hispanic black and 1,201 non-Hispanic white). We also investigated CUD interactions with current PTSD status and examined potential indirect effects of DNA methylation (DNAm) on the relationship between CUD and psychiatric diagnoses. Four CpGs were associated with lifetime CUD, even after controlling for the effects of current smoking (AHRR cg05575921, LINC00299 cg23079012, VWA7 cg22112841, and FAM70A cg08760398). Importantly, cg05575921, a CpG strongly linked to smoking, remained associated with lifetime CUD even when restricting the analysis to veterans who reported never smoking cigarettes. Moreover, CUD interacted with current PTSD to affect cg05575921 and cg23079012 such that those with both CUD and PTSD displayed significantly lower DNAm compared to the other groups. Finally, we provide preliminary evidence that AHRR cg05575921 helps explain the association between CUD and any psychiatric diagnoses, specifically mood disorders.

Nontargeted Plasma Proteomic Analysis of Renal Disease and Pulmonary Hypertension in Patients with Sickle Cell Disease.

Sickle cell disease (SCD) is characterized by red blood cell sickling, vaso-occlusion, hemolytic anemia, damage to multiple organ systems, and, as a result, shortened life expectancy. Sickle cell disease nephropathy (SCDN) and pulmonary hypertension (pHTN) are common and frequently co-occurring complications of SCD; both are associated with markedly accelerated mortality. To identify candidate circulating biomarkers of SCDN and pHTN, we used mass spectrometry to quantify the relative abundance of >1000 proteins in plasma samples from 189 adults with SCD from the Outcome Modifying Genes in SCD (OMG-SCD) cohort (ProteomeXchange identifier PXD048716). Forty-four proteins were differentially abundant in SCDN, most significantly cystatin-C and collagen α-1(XVIII) chain (COIA1), and 55 proteins were dysregulated in patients with SCDN and pHTN, most significantly insulin-like growth factor-binding protein 6 (IBP6). Network analysis identified a module of 133 coregulated proteins significantly associated with SCDN, that was enriched for extracellular matrix proteins, insulin-like growth factor binding proteins, cell adhesion proteins, EGF-like calcium binding proteins, and several cadherin family members. Collectively, these data provide a comprehensive understanding of plasma protein changes in SCDN and pHTN which validate numerous studies of chronic kidney disease and suggest shared profiles of protein disruption in kidney dysfunction and pHTN among SCD patients.

Posttraumatic stress disorder, trauma, and accelerated biological aging among post-9/11 veterans.

People who experience trauma and develop posttraumatic stress disorder (PTSD) are at increased risk for poor health. One mechanism that could explain this risk is accelerated biological aging, which is associated with the accumulation of chronic diseases, disability, and premature mortality. Using data from 2309 post-9/11 United States military veterans who participated in the VISN 6 MIRECC's Post-Deployment Mental Health Study, we tested whether PTSD and trauma exposure were associated with accelerated rate of biological aging, assessed using a validated DNA methylation (DNAm) measure of epigenetic aging-DunedinPACE. Veterans with current PTSD were aging faster than those who did not have current PTSD, ß = 0.18, 95% CI [0.11, 0.27], p < .001. This effect represented an additional 0.4 months of biological aging each year. Veterans were also aging faster if they reported more PTSD symptoms, ß = 0.13, 95% CI [0.09, 0.16], p < 0.001, or higher levels of trauma exposure, ß = 0.09, 95% CI [0.05, 0.13], p < 0.001. Notably, veterans with past PTSD were aging more slowly than those with current PTSD, ß = -0.21, 95% CI [-0.35, -0.07], p = .003. All reported results accounted for age, gender, self-reported race/ethnicity, and education, and remained when controlling for smoking. Our findings suggest that an accelerated rate of biological aging could help explain how PTSD contributes to poor health and highlights the potential benefits of providing efficacious treatment to populations at increased risk of trauma and PTSD.

Characterizing epigenetic aging in an adult sickle cell disease cohort.

ABSTRACT: Sickle cell disease (SCD) affects ∼100 000 predominantly African American individuals in the United States, causing significant cellular damage, increased disease complications, and premature death. However, the contribution of epigenetic factors to SCD pathophysiology remains 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 5 published epigenetic clocks: Horvath, Hannum, PhenoAge, GrimAge, and DunedinPACE. We hypothesized that in chronic disease, such as SCD, individuals would demonstrate epigenetic age acceleration, but the results differed depending on the clock used. Recently developed clocks more consistently demonstrated acceleration (GrimAge, DunedinPACE). Additional demographic and clinical phenotypes were analyzed to explore their association 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). The SCD genotype was associated with 2 clocks (PhenoAge, P = .02; DunedinPACE, P < .001). Genetic ancestry, biological sex, ß-globin haplotypes, BCL11A rs11886868, and SCD 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 their predictive ability and utility for chronic diseases such as SCD.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.

Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer's disease brains.

BACKGROUND: The genetic underpinnings of late-onset Alzheimer's disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD. METHODS: Here, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs). RESULTS: Integrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs, including APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes. CONCLUSIONS: To our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Differences in the Circulating Proteome in Individuals with versus without Sickle Cell Trait.

BACKGROUND: Sickle cell trait affects approximately 8% of Black individuals in the United States, along with many other individuals with ancestry from malaria-endemic regions worldwide. While traditionally considered a benign condition, recent evidence suggests that sickle cell trait is associated with lower eGFR and higher risk of kidney diseases, including kidney failure. The mechanisms underlying these associations remain poorly understood. We used proteomic profiling to gain insight into the pathobiology of sickle cell trait. METHODS: We measured proteomics ( N =1285 proteins assayed by Olink Explore) using baseline plasma samples from 592 Black participants with sickle cell trait and 1:1 age-matched Black participants without sickle cell trait from the prospective Women's Health Initiative cohort. Age-adjusted linear regression was used to assess the association between protein levels and sickle cell trait. RESULTS: In age-adjusted models, 35 proteins were significantly associated with sickle cell trait after correction for multiple testing. Several of the sickle cell trait-protein associations were replicated in Black participants from two independent cohorts (Atherosclerosis Risk in Communities study and Jackson Heart Study) assayed using an orthogonal aptamer-based proteomic platform (SomaScan). Many of the validated sickle cell trait-associated proteins are known biomarkers of kidney function or injury ( e.g. , hepatitis A virus cellular receptor 1 [HAVCR1]/kidney injury molecule-1 [KIM-1], uromodulin [UMOD], ephrins), related to red cell physiology or hemolysis (erythropoietin [EPO], heme oxygenase 1 [HMOX1], and α -hemoglobin stabilizing protein) and/or inflammation (fractalkine, C-C motif chemokine ligand 2/monocyte chemoattractant protein-1 [MCP-1], and urokinase plasminogen activator surface receptor [PLAUR]). A protein risk score constructed from the top sickle cell trait-associated biomarkers was associated with incident kidney failure among those with sickle cell trait during Women's Health Initiative follow-up (odds ratio, 1.32; 95% confidence interval, 1.10 to 1.58). CONCLUSIONS: We identified and replicated the association of sickle cell trait with a number of plasma proteins related to hemolysis, kidney injury, and inflammation.

Large epigenome-wide association study identifies multiple novel differentially methylated CpG sites associated with suicidal thoughts and behaviors in veterans.

Introduction: The U.S. suicide mortality rate has steadily increased during the past two decades, particularly among military veterans; however, the epigenetic basis of suicidal thoughts and behaviors (STB) remains largely unknown. Methods: To address this issue, we conducted an epigenome-wide association study of DNA methylation (DNAm) of peripheral blood samples obtained from 2,712 U.S. military veterans. Results: Three DNAm probes were significantly associated with suicide attempts, surpassing the multiple testing threshold (FDR q-value <0.05), including cg13301722 on chromosome 7, which lies between the genes SLC4A2 and CDK5; cg04724646 in PDE3A; and cg04999352 in RARRES3. cg13301722 was also found to be differentially methylated in the cerebral cortex of suicide decedents in a publicly-available dataset (p = 0.03). Trait enrichment analysis revealed that the CpG sites most strongly associated with STB in the present sample were also associated with smoking, alcohol consumption, maternal smoking, and maternal alcohol consumption, whereas pathway enrichment analysis revealed significant associations with circadian rhythm, adherens junction, insulin secretion, and RAP-1 signaling, each of which was recently associated with suicide attempts in a large, independent genome-wide association study of suicide attempts of veterans. Discussion: Taken together, the present findings suggest that SLC4A2, CDK5, PDE3A, and RARRES3 may play a role in STB. CDK5, a member of the cyclin-dependent kinase family that is highly expressed in the brain and essential for learning and memory, appears to be a particularly promising candidate worthy of future study; however, additional work is still needed to replicate these finding in independent samples.

Genome-wide association study identifies four pan-ancestry loci for suicidal ideation in the Million Veteran Program.

Suicidal ideation (SI) often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The genetic architecture of SI in the absence of suicide attempt (SA) is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. We performed the first GWAS of SI without SA in the Million Veteran Program (MVP), identifying 99,814 SI cases from electronic health records without a history of SA or suicide death (SD) and 512,567 controls without SI, SA or SD. GWAS was performed separately in the four largest ancestry groups, controlling for sex, age and genetic substructure. Ancestry-specific results were combined via meta-analysis to identify pan-ancestry loci. Four genome-wide significant (GWS) loci were identified in the pan-ancestry meta-analysis with loci on chromosomes 6 and 9 associated with suicide attempt in an independent sample. Pan-ancestry gene-based analysis identified GWS associations with DRD2, DCC, FBXL19, BCL7C, CTF1, ANNK1, and EXD3. Gene-set analysis implicated synaptic and startle response pathways (q's<0.05). European ancestry (EA) analysis identified GWS loci on chromosomes 6 and 9, as well as GWS gene associations in EXD3, DRD2, and DCC. No other ancestry-specific GWS results were identified, underscoring the need to increase representation of diverse individuals. The genetic correlation of SI and SA within MVP was high (rG = 0.87; p = 1.09e-50), as well as with post-traumatic stress disorder (PTSD; rG = 0.78; p = 1.98e-95) and major depressive disorder (MDD; rG = 0.78; p = 8.33e-83). Conditional analysis on PTSD and MDD attenuated most pan-ancestry and EA GWS signals for SI without SA to nominal significance, with the exception of EXD3 which remained GWS. Our novel findings support a polygenic and complex architecture for SI without SA which is largely shared with SA and overlaps with psychiatric conditions frequently comorbid with suicidal behaviors.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results.

Studies combining metabolomics and genetics, known as metabolite genome-wide association studies (mGWAS), have provided valuable insights into our understanding of the genetic control of metabolite levels. However, the biological interpretation of these associations remains challenging due to a lack of existing tools to annotate mGWAS gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we computed the shortest reactional distance (SRD) based on the curated knowledge of the KEGG database to explore its utility in enhancing the biological interpretation of results from three independent mGWAS, including a case study on sickle cell disease patients. Results show that, in reported mGWAS pairs, there is an excess of small SRD values and that SRD values and p-values significantly correlate, even beyond the standard conservative thresholds. The added-value of SRD annotation is shown for identification of potential false negative hits, exemplified by the finding of gene-metabolite associations with SRD ≤1 that did not reach standard genome-wide significance cut-off. The wider use of this statistic as an mGWAS annotation would prevent the exclusion of biologically relevant associations and can also identify errors or gaps in current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs that can be used to integrate statistical evidence to biological networks.

Genetic regulation of fetal hemoglobin across global populations.

Human genetic variation has enabled the identification of several key regulators of fetal-to-adult hemoglobin switching, including BCL11A, resulting in therapeutic advances. However, despite the progress made, limited further insights have been obtained to provide a fuller accounting of how genetic variation contributes to the global mechanisms of fetal hemoglobin (HbF) gene regulation. Here, we have conducted a multi-ancestry genome-wide association study of 28,279 individuals from several cohorts spanning 5 continents to define the architecture of human genetic variation impacting HbF. We have identified a total of 178 conditionally independent genome-wide significant or suggestive variants across 14 genomic windows. Importantly, these new data enable us to better define the mechanisms by which HbF switching occurs in vivo. We conduct targeted perturbations to define BACH2 as a new genetically-nominated regulator of hemoglobin switching. We define putative causal variants and underlying mechanisms at the well-studied BCL11A and HBS1L-MYB loci, illuminating the complex variant-driven regulation present at these loci. We additionally show how rare large-effect deletions in the HBB locus can interact with polygenic variation to influence HbF levels. Our study paves the way for the next generation of therapies to more effectively induce HbF in sickle cell disease and ß-thalassemia.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Variation and impact of polygenic hematologic traits in monogenic sickle cell disease.

Several of the complications observed in sickle cell disease (SCD) are influenced by variation in hematologic traits (HT), such as fetal hemoglobin (HbF) level and neutrophil count. Previous large-scale genome-wide association studies carried out in largely healthy individuals have identified thousands of variants associated with HT, which have then been used to develop multi-ancestry polygenic trait scores (PTS). Here, we tested whether these PTS associate with HT in SCD patients and if they can improve statistical models associated with SCD-related complications. In 2,056 SCD patients, we found that the PTS predicted less HT variance than in non-SCD individuals of African ancestry. This was particularly striking at the Duffy/DARC locus, where we observed an epistatic interaction between the SCD genotype and the Duffy null variant (rs2814778) that led to a two-fold weaker effect on neutrophil count. PTS for these HT which are measured as part of routine practice were not associated with complications in SCD. In contrast, we found that a simple PTS for HbF that includes only six variants explained a large fraction of the phenotypic variation (20.5-27.1%), associated with acute chest syndrome and stroke risk, and improved the statistical modeling of the vaso-occlusive crisis rate. Using Mendelian randomization, we found that increasing HbF by 4.8% reduces stroke risk by 39% (P=0.0006). Taken together, our results highlight the importance of validating PTS in large diseased populations before proposing their implementation in the context of precision medicine initiatives.