Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies

Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.     

Integrating Multiple Genomic Data to Predict Disease-Causing Nonsynonymous Single Nucleotide Variants in Exome Sequencing Studies

Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (Snv PRioritization via the INtegration of Genomic data) for identifying pathogenic nonsynonymous SNVs for a given query disease. Based on six functional effect scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations), SPRING calculates the statistical significance that an SNV is causative for a query disease and hence provides a means of prioritizing candidate SNVs. With a series of comprehensive validation experiments, we demonstrate that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown and effective for diseases with a variety of inheritance styles. In applications of our method to real exome sequencing data sets, we show the capability of SPRING in detecting causative de novo mutations for autism, epileptic encephalopathies and intellectual disability. We further provide an online service, the standalone software and genome-wide predictions of causative SNVs for 5,080 diseases at http://bioinfo.au.tsinghua.edu.cn/spring.     

Association Testing of Clustered Rare Causal Variants in Case-Control Studies

Biological evidence suggests that multiple causal variants in a gene may cluster physically. Variants within the same protein functional domain or gene regulatory element would locate in close proximity on the DNA sequence. However, spatial information of variants is usually not used in current rare variant association analyses. We here propose a clustering method (abbreviated as “CLUSTER”), which is extended from the adaptive combination of P-values. Our method combines the association signals of variants that are more likely to be causal. Furthermore, the statistic incorporates the spatial information of variants. With extensive simulations, we show that our method outperforms several commonly-used methods in many scenarios. To demonstrate its use in real data analyses, we also apply this CLUSTER test to the Dallas Heart Study data. CLUSTER is among the best methods when the effects of causal variants are all in the same direction. As variants located in close proximity are more likely to have similar impact on disease risk, CLUSTER is recommended for association testing of clustered rare causal variants in case-control studies.     

Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism

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Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression

Although many methods are available to test sequence variants for association with complex diseases and traits, methods that specifically seek to identify causal variants are less developed. Here we develop and evaluate a Bayesian hierarchical regression method that incorporates prior information on the likelihood of variant causality through weighting of variant effects. By simulation studies using both simulated and real sequence variants, we compared a standard single variant test for analyzing variant-disease association with the proposed method using different weighting schemes. We found that by leveraging linkage disequilibrium of variants with known GWAS signals and sequence conservation (phastCons), the proposed method provides a powerful approach for detecting causal variants while controlling false positives.     

A Method to Prioritize Quantitative Traits and Individuals for Sequencing in Family-Based Studies

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.     

Wastewater-Based Epidemiology of Stimulant Drugs: Functional Data Analysis Compared to Traditional Statistical Methods

Background

Wastewater-based epidemiology (WBE) is a new methodology for estimating the drug load in a population. Simple summary statistics and specification tests have typically been used to analyze WBE data, comparing differences between weekday and weekend loads. Such standard statistical methods may, however, overlook important nuanced information in the data. In this study, we apply functional data analysis (FDA) to WBE data and compare the results to those obtained from more traditional summary measures.

Methods

We analysed temporal WBE data from 42 European cities, using sewage samples collected daily for one week in March 2013. For each city, the main temporal features of two selected drugs were extracted using functional principal component (FPC) analysis, along with simpler measures such as the area under the curve (AUC). The individual cities’ scores on each of the temporal FPCs were then used as outcome variables in multiple linear regression analysis with various city and country characteristics as predictors. The results were compared to those of functional analysis of variance (FANOVA).

Results

The three first FPCs explained more than 99% of the temporal variation. The first component (FPC1) represented the level of the drug load, while the second and third temporal components represented the level and the timing of a weekend peak. AUC was highly correlated with FPC1, but other temporal characteristic were not captured by the simple summary measures. FANOVA was less flexible than the FPCA-based regression, and even showed concordance results. Geographical location was the main predictor for the general level of the drug load.

Conclusion

FDA of WBE data extracts more detailed information about drug load patterns during the week which are not identified by more traditional statistical methods. Results also suggest that regression based on FPC results is a valuable addition to FANOVA for estimating associations between temporal patterns and covariate information.     

Revealing Molecular Mechanisms by Integrating High-Dimensional Functional Screens with Protein Interaction Data

Functional genomics screens using multi-parametric assays are powerful approaches for identifying genes involved in particular cellular processes. However, they suffer from problems like noise, and often provide little insight into molecular mechanisms. A bottleneck for addressing these issues is the lack of computational methods for the systematic integration of multi-parametric phenotypic datasets with molecular interactions. Here, we present Integrative Multi Profile Analysis of Cellular Traits (IMPACT). The main goal of IMPACT is to identify the most consistent phenotypic profile among interacting genes. This approach utilizes two types of external information: sets of related genes (IMPACT-sets) and network information (IMPACT-modules). Based on the notion that interacting genes are more likely to be involved in similar functions than non-interacting genes, this data is used as a prior to inform the filtering of phenotypic profiles that are similar among interacting genes. IMPACT-sets selects the most frequent profile among a set of related genes. IMPACT-modules identifies sub-networks containing genes with similar phenotype profiles. The statistical significance of these selections is subsequently quantified via permutations of the data. IMPACT (1) handles multiple profiles per gene, (2) rescues genes with weak phenotypes and (3) accounts for multiple biases e.g. caused by the network topology. Application to a genome-wide RNAi screen on endocytosis showed that IMPACT improved the recovery of known endocytosis-related genes, decreased off-target effects, and detected consistent phenotypes. Those findings were confirmed by rescreening 468 genes. Additionally we validated an unexpected influence of the IGF-receptor on EGF-endocytosis. IMPACT facilitates the selection of high-quality phenotypic profiles using different types of independent information, thereby supporting the molecular interpretation of functional screens.     

Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer

The HOXB13 gene has been implicated in prostate cancer (PrCa) susceptibility. We performed a high resolution fine-mapping analysis to comprehensively evaluate the association between common genetic variation across the HOXB genetic locus at 17q21 and PrCa risk. This involved genotyping 700 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of 3195 SNPs in 20,440 PrCa cases and 21,469 controls in The PRACTICAL consortium. We identified a cluster of highly correlated common variants situated within or closely upstream of HOXB13 that were significantly associated with PrCa risk, described by rs117576373 (OR 1.30, P = 2.62×10⁻¹⁴). Additional genotyping, conditional regression and haplotype analyses indicated that the newly identified common variants tag a rare, partially correlated coding variant in the HOXB13 gene (G84E, rs138213197), which has been identified recently as a moderate penetrance PrCa susceptibility allele. The potential for GWAS associations detected through common SNPs to be driven by rare causal variants with higher relative risks has long been proposed; however, to our knowledge this is the first experimental evidence for this phenomenon of synthetic association contributing to cancer susceptibility.     

Identification of Rare Causal Variants in Sequence-Based Studies: Methods and Applications to VPS13B,a Gene Involved in Cohen Syndrome and Autism

Pinpointing the small number of causal variants among the abundant naturally occurring genetic variation is a difficult challenge, but a crucial one for understanding precise molecular mechanisms of disease and follow-up functional studies. We propose and investigate two complementary statistical approaches for identification of rare causal variants in sequencing studies: a backward elimination procedure based on groupwise association tests, and a hierarchical approach that can integrate sequencing data with diverse functional and evolutionary conservation annotations for individual variants. Using simulations, we show that incorporation of multiple bioinformatic predictors of deleteriousness, such as PolyPhen-2, SIFT and GERP++ scores, can improve the power to discover truly causal variants. As proof of principle, we apply the proposed methods to VPS13B, a gene mutated in the rare neurodevelopmental disorder called Cohen syndrome, and recently reported with recessive variants in autism. We identify a small set of promising candidates for causal variants, including two loss-of-function variants and a rare, homozygous probably-damaging variant that could contribute to autism risk.     

Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma

Development of post-GWAS (genome-wide association study) methods are greatly needed for characterizing the function of trait-associated SNPs. Strategies integrating various biological data sets with GWAS results will provide insights into the mechanistic role of associated SNPs. Here, we present a method that integrates RNA sequencing (RNA-seq) and allele-specific expression data with GWAS data to further characterize SNPs associated with follicular lymphoma (FL). We investigated the influence on gene expression of three established FL-associated loci—rs10484561, rs2647012, and rs6457327—by measuring their correlation with human-leukocyte-antigen (HLA) expression levels obtained from publicly available RNA-seq expression data sets from lymphoblastoid cell lines. Our results suggest that SNPs linked to the protective variant rs2647012 exert their effect by a cis-regulatory mechanism involving modulation of HLA-DQB1 expression. In contrast, no effect on HLA expression was observed for the colocalized risk variant rs10484561. The application of integrative methods, such as those presented here, to other post-GWAS investigations will help identify causal disease variants and enhance our understanding of biological disease mechanisms.     

A Theoretical Model for Utilizing Mammalian Pharmacology and Safety Data to Prioritize Potential Impacts of Human Pharmaceuticals to Fish

Due to the potential for long-term, low-level exposure of environmental species to pharmaceuticals in the environment, concerns over chronic ecotoxicity have been raised. Pharmaceuticals typically have specific enzyme and receptor-based modes of action, which are extensively studied in mammals during drug development. A survey of the literature demonstrated that there is conservation of many enzyme/receptor systems between mammalian and teleost systems. Based on this conservation of enzyme/receptor systems across teleost species, a model has been developed to utilize the information from mammalian pharmacology and toxicology studies to evaluate the potential for chronic receptor mediated responses in fish. In this model, a measured human therapeutic plasma concentration (H_TPC) is compared to a predicted steady state plasma concentration (F_SSPC) in fish, and an effect ratio (ER = H_TPC/F_SSPC) is computed. The lower the ER, the greater the potential for a pharmacological response in fish. Data collection and model validation will strengthen the applicability of this approach as a viable tool for prioritizing research initiatives that examine the potential impact of pharmaceuticals on fish.