Publications

BACKGROUND

Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

RESULTS

In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6).

CONCLUSION

We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

BACKGROUND AND PURPOSE

Established risk factors do not fully identify patients at risk for recurrent stroke. The SPARCL trial (Stroke Prevention by Aggressive Reduction in Cholesterol Levels) evaluated the effect of atorvastatin on stroke risk in patients with a recent stroke or transient ischemic attack and no known coronary heart disease. This analysis explored the relationships between 13 plasma biomarkers assessed at trial enrollment and the occurrence of outcome strokes.

METHODS

We conducted a case-cohort study of 2176 participants; 562 had outcome strokes and 1614 were selected randomly from those without outcome strokes. Time to stroke was evaluated by Cox proportional hazards models.

RESULTS

There was no association between time to stroke and lipoprotein-associated phospholipase A, monocyte chemoattractant protein-1, resistin, matrix metalloproteinase-9, N-terminal fragment of pro-B-type natriuretic peptide, soluble vascular cell adhesion molecule-1, soluble intercellular adhesion molecule-1, or soluble CD40 ligand. In adjusted analyses, osteopontin (hazard ratio per SD change, 1.362; <0.0001), neopterin (hazard ratio, 1.137; =0.0107), myeloperoxidase (hazard ratio, 1.177; =0.0022), and adiponectin (hazard ratio, 1.207; =0.0013) were independently associated with outcome strokes. After adjustment for the Stroke Prognostic Instrument-II and treatment, osteopontin, neopterin, and myeloperoxidase remained independently associated with outcome strokes. The addition of these 3 biomarkers to Stroke Prognostic Instrument-II increased the area under the receiver operating characteristic curve by 0.023 (=0.015) and yielded a continuous net reclassification improvement (29.1%; <0.0001) and an integrated discrimination improvement (42.3%; <0.0001).

CONCLUSIONS

Osteopontin, neopterin, and myeloperoxidase were independently associated with the risk of recurrent stroke and improved risk classification when added to a clinical risk algorithm.

CLINICAL TRIAL REGISTRATION

URL: http://www.clinicaltrials.gov. Unique Identifier: NCT00147602.