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Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to ‘pleiotropy’ and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn’s disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Background For the second aim of the Kellogg Foundation grant, this double-blind RCT investigated the impact of plasma vitamin D metabolite 25-hydroxyvitamin D (25(OH)D) on plasma immune-mediators during pregnancy. We hypothesized that higher 25(OH)D concentrations would associate with reduced pro-inflammatory and increased tolerogenic immune-mediator concentrations. Methods Pregnant women enrolled at 10–14 weeks gestation were randomized to 400 or 4400 IU vitamin D 3 /day. Data on health, safety, circulating 25(OH)D, and 9 immune-mediators were collected at each trimester. Associations between immune-mediators and 25(OH)D at baseline and at second and third trimesters were examined. Results Baseline TGF-β and second and third trimesters IFN-γ and IL-2 were associated with baseline 25(OH)D. Baseline immune-mediators were associated with immune-mediators at second and third trimesters for all immune-mediators except IL-5 and IL-10. Race was associated with baseline TGF-β, VEGF and IL-10 and with IL-10 at second and third trimesters. Conclusions Both treatment groups had increased 25(OH)D at second and third trimesters, greatest in the 4400 IU group. Though associations between baseline 25(OH)D and baseline TGF-β and second and third trimester IFN-γ and IL-2 were noted, vitamin D supplementation throughout pregnancy did not impact immune-mediators at later trimesters. Supplementing with vitamin D before conception conceivably influences immune-mediator responses during pregnancy. Impact In this vitamin D supplementation clinical trial, baseline (first trimester) but not increasing plasma 25(OH)D concentration impacted select plasma immune-mediator profiles in pregnant women. Baseline 25(OH)D was associated with baseline TGF-β and with IFN-γ and IL-2 at second and third trimesters. Baseline IFN-γ, CRP, TGF-β, TNF-α, VEGF, IL-2, and IL-4 were associated with concentrations at second and third trimesters for respective immune-mediators; however, 25(OH)D concentration at second and third trimesters were not. Some racial differences existed in immune-mediator concentrations at baseline and at second and third trimesters. This study assesses the impact of vitamin D supplementation on multiple immune-mediators in pregnant women of different racial/ethnic groups using longitudinal data from a relatively large randomized controlled trial. This study found that race was associated with baseline TGF-β, VEGF, and IL-10 and with IL-10 at second and third trimesters, a novel finding that sheds light where relationships were less well defined. The results of this study suggest that vitamin D supplementation before conception or early in pregnancy, rather than during pregnancy, may be necessary to significantly impact immune-mediator response. This study sets premise for future clinical trials to evaluate the effect of vitamin D supplementation before conception or prior to pregnancy.

Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. Methods Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. Results Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8 + T cells, CD4 + T cells, and CD56 + natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. Conclusions Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19.

ObjectiveThis study investigated changes in kidney histology over time in patients with lupus nephritis (LN) undergoing immunosuppressive treatment.MethodsPatients with proliferative±membranous LN were studied. After a diagnostic kidney biopsy (Bx1), patients had protocol biopsy 2 (Bx2) at 9 (6–15) months and protocol biopsy 3 (Bx3) at 42 (28–67) months. Kidney histological activity and chronicity indices (AI, CI) were measured.ResultsAI declined in a biphasic fashion, falling rapidly between Bx1 and Bx2 and then more slowly between Bx2 and Bx3. Patients were divided into those who achieved histological remission, defined as an AI=0 at Bx3 (group 1), and those with persistent histological activity (AI >0) at Bx3 (group 2). The early decline in AI was 1.6 times greater (95% CI 1.30, 1.91) in group 1 than group 2 (p=0.01). Between Bx2 and Bx3, the AI decline was 2.19-fold greater (95% CI 2.09, 2.29) in group 1 versus group 2 (p=7.34×10−5). Individual histological components of the AI resolved at different rates. Inflammatory lesions like glomerular crescents, karyorrhexis and necrosis mostly resolved by Bx2, whereas endocapillary hypercellularity, subendothelial hyaline deposits and interstitial inflammation resolved slowly, accounting for residual histological activity at biopsy 3 in group 2. In contrast, CI increased rapidly, by 0.15 units/month between Bx1 and Bx2, then plateaued. There were no differences in the rate of accumulation of chronic damage between group 1 and group 2. The increase in CI was significantly related to the severity of glomerular crescents (p=0.044), subendothelial hyaline deposits (p=0.002) and interstitial inflammation (p=0.015) at Bx1.ConclusionsLN histological activity takes months to years to resolve, providing a rationale for the need of long-term, well-tolerated maintenance immunosuppression. Despite responding, LN kidneys accrue chronic damage early during treatment. This finding provides an explanation for the association of chronic progressive kidney disease with recurrent episodes of LN.

Longitudinal data arise when individuals are measured several times during an observation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are compared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to `pleiotropy' and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with an existing statistical approach.The results indicate that multi-GPA-Tree outperforms the existing statistical approach in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn's disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

Motivation: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. Results: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.