Leveraging functional annotations in genetic risk prediction for human complex diseases.
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ABSTRACT:
SUBMITTER: Hu Y
PROVIDER: S-EPMC5481142 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Accurate prediction of disease risk based on genetic factors is an important goal in human genetics research and precision medicine. Advanced prediction models will lead to more effective disease prevention and treatment strategies. Despite the identification of thousands of disease-associated genetic variants through genome-wide association studies (GWAS) in the past decade, accuracy of genetic risk prediction remains moderate for most diseases, which is largely due to the challenges in both identifying all the functionally relevant variants and accurately estimating their effect sizes. In this work, we introduce PleioPred, a principled framework that leverages pleiotropy and functional annotations in genetic risk prediction for complex diseases. PleioPred uses GWAS summary statistics as its input, and jointly models multiple genetically correlated diseases and a variety of external information including linkage disequilibrium and diverse functional annotations to increase the accuracy of risk prediction. Through comprehensive simulations and real data analyses on Crohn's disease, celiac disease and type-II diabetes, we demonstrate that our approach can substantially increase the accuracy of polygenic risk prediction and risk population stratification, i.e. PleioPred can significantly better separate type-II diabetes patients with early and late onset ages, illustrating its potential clinical application. Furthermore, we show that the increment in prediction accuracy is significantly correlated with the genetic correlation between the predicted and jointly modeled diseases.
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Project description:Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learning models, DeepSEA and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K), conditioning on a broad set of coding, conserved and regulatory annotations. We aggregated annotations across all (respectively blood or brain) tissues/cell-types in meta-analyses across all (respectively 11 blood or 8 brain) traits. The annotations were highly enriched for disease heritability, but produced only limited conditionally significant results: non-tissue-specific and brain-specific Basenji-H3K4me3 for all traits and brain traits respectively. We conclude that deep learning models have yet to achieve their full potential to provide considerable unique information for complex disease, and that their conditional informativeness for disease cannot be inferred from their accuracy in predicting regulatory annotations.
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Project description:Analysis of the biological pathways involved in complex human diseases is an important step in elucidating the pathogenesis and mechanism of diseases. Most pathway analysis approaches identify disease-related biological pathways using overlapping genes between pathways and diseases. However, these approaches ignore the functional biological association between pathways and diseases. In this paper, we designed a novel computational framework for prioritising disease-risk pathways based on functional profiling. The disease gene set and biological pathways were translated into functional profiles in the context of GO annotations. We then implemented a semantic similarity measurement for calculating the concordance score between a functional profile of disease genes and a functional profile of pathways (FPP); the concordance score was then used to prioritise and infer disease-risk pathways. A freely accessible web toolkit, 'Functional Profiling-based Pathway Prioritisation' (FPPP), was developed (http://bioinfo.hrbmu.edu.cn/FPPP). During validation, our method successfully identified known disease-pathway pairs with area under the ROC curve (AUC) values of 96.73 and 95.02% in tests using both pathway randomisation and disease randomisation. A robustness analysis showed that FPPP is reliable even when using data containing noise. A case study based on a dilated cardiomyopathy data set indicated that the high-ranking pathways from FPPP are well known to be linked with this disease. Furthermore, we predicted the risk pathways of 413 diseases by using FPPP to build a disease similarity landscape that systematically reveals the global modular organisation of disease associations.
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Project description:Some genes involved in complex human diseases are particularly vulnerable to genetic variations such as single nucleotide polymorphism, copy number variations, and mutations. For example, Ras mutations account for over 30 % of all human cancers. Additionally, there are some genes that can display different variations with functional impact in different diseases that are unrelated. One such gene stands out: ?-catenin/NPRAP/Neurojungin with gene designation as CTNND2 on chromosome 5p15.2. Recent advances in genome wide association as well as molecular biology approaches have uncovered striking involvement of ?-catenin gene variations linked to complex human disorders. These disorders include cancer, bipolar disorder, schizophrenia, autism, Cri-du-chat syndrome, myopia, cortical cataract-linked Alzheimer's disease, and infectious diseases. This list has rapidly grown longer in recent years, underscoring the pivotal roles of ?-catenin in critical human diseases. ?-Catenin is an adhesive junction-associated protein in the delta subfamily of the ?-catenin superfamily. ?-Catenin functions in Wnt signaling to regulate gene expression and modulate Rho GTPases of the Ras superfamily in cytoskeletal reorganization. ?-Catenin likely lies where Wnt signaling meets Rho GTPases and is a unique and vulnerable common target for mutagenesis in different human diseases.
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Project description:There is increasing evidence that many risk loci found using genome-wide association studies are molecular quantitative trait loci (QTLs). Here we introduce a new set of functional annotations based on causal posterior probabilities of fine-mapped molecular cis-QTLs, using data from the Genotype-Tissue Expression (GTEx) and BLUEPRINT consortia. We show that these annotations are more strongly enriched for heritability (5.84× for eQTLs; P = 1.19 × 10-31) across 41 diseases and complex traits than annotations containing all significant molecular QTLs (1.80× for expression (e)QTLs). eQTL annotations obtained by meta-analyzing all GTEx tissues generally performed best, whereas tissue-specific eQTL annotations produced stronger enrichments for blood- and brain-related diseases and traits. eQTL annotations restricted to loss-of-function intolerant genes were even more enriched for heritability (17.06×; P = 1.20 × 10-35). All molecular QTLs except splicing QTLs remained significantly enriched in joint analysis, indicating that each of these annotations is uniquely informative for disease and complex trait architectures.
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Project description:The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease.A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve.Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone.Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.
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