Xinwei Yao | Search bioRxiv

bioRxiv2016

Leveraging Functional Annotations in Genetic Risk Prediction for Human Complex Diseases

Genome wide association studies have identified numerous regions in the genome associated with hundreds of human diseases. Building accurate genetic risk prediction models from these data will have great impacts on disease prevention and treatment strategies. However, prediction accuracy remains moderate for most diseases, which is largely due to the challenges in identifying all the disease-associated variants and accurately estimating their effect sizes. We introduce AnnoPred, a principled framework that incorporates diverse functional annotation data to improve risk prediction accuracy, and demonstrate its performance on multiple human complex diseases.

Bioinformatics↗

bioRxiv2015

GenoWAP: Post-GWAS Prioritization Through Integrated Analysis of Genomic Functional Annotation

MotivationGenome-wide association study (GWAS) has been a great success in the past decade. However, significant challenges still remain in both identifying new risk loci and interpreting results. Bonferroni-corrected significance level is known to be conservative, leading to insufficient statistical power when the effect size is moderate at risk locus. Complex structure of linkage disequilibrium also makes it challenging to separate causal variants from nonfunctional ones in large haplotype blocks.\n\nResultsWe describe GenoWAP, a post-GWAS prioritization method that integrates genomic functional annotation and GWAS test statistics. The effectiveness of GenoWAP is demonstrated through its applications to Crohns disease and schizophrenia using the largest studies available, where highly ranked loci show substantially stronger signals in the whole dataset after prioritization based on a subset of samples. At the single nucleotide polymorphism (SNP) level, top ranked SNPs after prioritization have both higher replication rates and consistently stronger enrichment of eQTLs. Within each risk locus, GenoWAP is also able to distinguish functional sites from groups of correlated SNPs.\n\nAvailability and ImplementationGenoWAP is freely available on the web at http://genocanyon.med.yale.edu/GenoWAP

Bioinformatics↗