GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results

multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results

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.

Study on the Relationship between lncRNA Gene Polymorphism and Systemic Lupus Erythematosus

Background

Systemic lupus erythematosus (SLE) is an autoimmune disease that can cause damage to multiple systems of the body. A number of studies have shown that long-chain noncoding RNA (lncRNA) can participate in the occurrence and development of a variety of autoimmune diseases. This study is aimed at detecting the expression levels of 5 lncRNAs in SLE patients and healthy controls and at exploring the relationship between expression levels and clinical symptoms and laboratory indicators.

Methods

The design type of this study is a case-control study. A total of 76 SLE patients and 71 healthy controls were included in the first phase of the study. Real-time fluorescence quantitative polymerase chain reaction was used to detect the expression level of 5 kinds of lncRNAs including lnc7514, lnc0640, lncagf, nc3643, and lnc5150 in PBMCs of two groups of patients; the expression of lncRNAs in the case group and the control group was analyzed. We analyzed the differences in the expression levels of lncRNAs between case and control groups, and explored the association of expression levels with clinical manifestations and laboratory characteristics. SPSS23.0 was used to analyze the expression level and gene polymorphism results; the statistical analysis test level α = 0.05.

Results

The expression level of lnc0640 in PBMCs of SLE patient group was higher than that of healthy control group (Z = -3.56, P = 0.03). However, lnc5150 was lower than in healthy controls (Z = -7.16, P < 0.001). lnc3643 expression levels were lower in SLE patients of SLE patients with pleurisy was lower than that of patients without pleurisy (Z = -2.44, P = 0.02). Low lnc3643 expression levels were observed in PBMCs with SLE patients with rash symptoms (Z = -2.75, P = 0.013). SLE expressed lower lnc3643 levels in PBMCs with SLE compared with those without pleurisy (Z = -2.42, P = 0.02). The above differences were statistically significant. Association analysis of lncRNA expression levels and clinical manifestations in SLE patients found that SLE was lower than those without rash or pleurisy (both P < 0.05); association analysis of lncRNA expression level and laboratory results found a negative correlation between lnc3643, lnc7514, and SLE disease activity score (SLEDAI-2K), blood sink (ESR), and C-reactive protein (CRP) (all P < 0.05).

Conclusions

lnc0640 was overexpressed in PBMCs in SLE patients compared with healthy controls. lnc3643 was negatively correlated with SLEDAI, and expression levels were associated with SLE patients with arthritis, rash, and pleuritis.