Publications

Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly being viewed as useful supplements to the more widely used single marker association analysis which have successfully uncovered numerous disease variants. A major drawback of single-marker based methods is that they do not look at the joint effects of multiple genetic variants which individually may have weak or moderate signals. Here, we describe novel tests for multi-marker association analyses that are based on phenotype predictions obtained from machine learning algorithms. Instead of assuming a linear or logistic regression model, we propose the use of ensembles of diverse machine learning algorithms for prediction. We show that phenotype predictions obtained from ensemble learning algorithms provide a new framework for multi-marker association analysis. They can be used for constructing tests for the joint association of multiple variants, adjusting for covariates and testing for the presence of interactions. To demonstrate the power and utility of this new approach, we first apply our method to simulated SNP datasets. We show that the proposed method has the correct Type-1 error rates and can be considerably more powerful than alternative approaches in some situations. Then, we apply our method to previously studied asthma-related genes in 2 independent asthma cohorts to conduct association tests.

BACKGROUND

Liver disease is common during human immunodeficiency virus (HIV) infection, but valid serum fibrosis markers are lacking. We hypothesize that HIV monoinfection and HIV/hepatitis C virus (HCV) coinfection is associated with an enhanced liver fibrosis (ELF) score higher than that for uninfected controls and examine whether this association is affected by factors other than liver injury.

METHODS

The association of HIV and HIV/HCV coinfection with the ELF score was evaluated using multivariable regression after controlling for transient elastography-measured liver stiffness and traditional and HIV-related factors in a cross-sectional analysis of 297 women.

RESULTS

HIV/HCV-coinfected and HIV-monoinfected women had higher median ELF scores than controls (9.6, 8.5, and 8.2, respectively). After adjustment for demographic, behavioral, and metabolic factors and for inflammatory markers, HIV/HCV coinfection remained associated with a 9% higher ELF score (95% confidence interval [CI], 5%-13%), while the association of HIV monoinfection was substantially attenuated (1% higher ELF score; 95% CI, -2% to 4%). After further adjustment for liver stiffness, HIV/HCV coinfection remained associated with 6% higher levels (95% CI, 3%-10%). In HIV/HCV-coinfected and HIV-monoinfected women, higher liver stiffness values were associated with higher ELF scores, as were older age and a nadir CD4(+) T-cell count of <200 cells/mm(3).

CONCLUSIONS

Our findings suggest that the ELF score can be used to assess liver fibrosis severity in HIV-infected women. However, higher ELF scores may reflect extrahepatic fibrosis in HIV-infected patients with a history of severe immunosuppression or advanced age.

This study is to evaluate highly accelerated three-dimensional (3D) dynamic contrast-enhanced (DCE) wrist MRI for assessment of perfusion in rheumatoid arthritis (RA) patients. A pseudo-random variable-density undersampling strategy, circular Cartesian undersampling (CIRCUS), was combined with k-t SPARSE-SENSE reconstruction to achieve a highly accelerated 3D DCE wrist MRI. Two healthy volunteers and 10 RA patients were studied. Two patients were on methotrexate (MTX) only (Group I) and the other eight were treated with a combination therapy of MTX and anti-tumor necrosis factor (TNF) therapy (Group II). Patients were scanned at baseline and 3 month follow-up. DCE MR images were used to evaluate perfusion in synovitis and bone marrow edema pattern in the RA wrist joints. A series of perfusion parameters was derived and compared with clinical disease activity scores of 28 joints (DAS28). 3D DCE wrist MR images were obtained with a spatial resolution of 0.3 × 0.3 × 1.5 mm(3) and temporal resolution of 5 s (with an acceleration factor of 20). The derived perfusion parameters, most notably transition time (dT) of synovitis, showed significant negative correlations with DAS28-ESR (r = -0.80, p < 0.05) and DAS28-CRP (r = -0.87, p < 0.05) at baseline and also correlated significantly with treatment responses evaluated by clinical score changes between baseline and 3 month follow-up (with DAS28-ESR r = -0.79, p < 0.05, and DAS28-CRP r = -0.82, p < 0.05). Highly accelerated 3D DCE wrist MRI with improved temporospatial resolution has been achieved in RA patients and provides accurate assessment of neovascularization and perfusion in RA joints, showing promise as a potential tool for evaluating treatment responses.