Functional annotation signatures of disease susceptibility loci improve SNP association analysis.

BACKGROUND: Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan. RESULTS: We developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations. CONCLUSIONS: We show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Mental Health and Substance Use Among Patients in a North Carolina HIV Clinic.

BACKGROUND: The HIV/AIDS epidemic is a significant public health concern in North Carolina, and previous research has pointed to elevated mental health distress and substance use among HIV-infected populations, which may impact patients' adherence to medications. The aims of this study were to describe the prevalence of mental health and substance use issues among patients of a North Carolina HIV clinic, to examine differences by demographic characteristics, and to examine factors associated with suboptimal adherence to HIV medications. METHODS: This study was a secondary analysis of clinical data routinely collected through a health behavior questionnaire at a large HIV clinic in North Carolina. We analyzed data collected from February 2011 to August 2012. RESULTS: The sample included 1,398 patients. Overall, 12.2% of patients endorsed current symptomology indicative of moderate or severe levels of depression, and 38.6% reported receiving a psychiatric diagnosis at some point in their life. Additionally, 19.1% had indications of current problematic drinking, and 8.2% reported problematic drug use. Nearly one-quarter (22.1%) reported suboptimal adherence to HIV medications. Factors associated with poor adherence included racial/ethnic minority, age less than 35 years, and indications of moderate or severe depression. LIMITATIONS: The questionnaire was not completed systematically in the clinic, which may limit generalizability, and self-reported measures may have introduced social desirability bias. CONCLUSION: Patients were willing to disclose mental health distress, substance use, and suboptimal medication adherence to providers, which highlights the importance of routinely assessing these behaviors during clinic visits. Our findings suggest that treating depression may be an effective strategy to improve adherence to HIV medications.