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Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations  [PDF]
Alexandra C. Nica,Stephen B. Montgomery,Antigone S. Dimas,Barbara E. Stranger,Claude Beazley,Inês Barroso,Emmanouil T. Dermitzakis
PLOS Genetics , 2010, DOI: 10.1371/journal.pgen.1000895
Abstract: The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.
Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification  [PDF]
Laura L. Faye,Mitchell J. Machiela,Peter Kraft on behalf of the Breast and Prostate Cancer Cohort Consortium,Shelley B. Bull,Lei Sun
PLOS Genetics , 2013, DOI: 10.1371/journal.pgen.1003609
Abstract: Next generation sequencing has dramatically increased our ability to localize disease-causing variants by providing base-pair level information at costs increasingly feasible for the large sample sizes required to detect complex-trait associations. Yet, identification of causal variants within an established region of association remains a challenge. Counter-intuitively, certain factors that increase power to detect an associated region can decrease power to localize the causal variant. First, combining GWAS with imputation or low coverage sequencing to achieve the large sample sizes required for high power can have the unintended effect of producing differential genotyping error among SNPs. This tends to bias the relative evidence for association toward better genotyped SNPs. Second, re-use of GWAS data for fine-mapping exploits previous findings to ensure genome-wide significance in GWAS-associated regions. However, using GWAS findings to inform fine-mapping analysis can bias evidence away from the causal SNP toward the tag SNP and SNPs in high LD with the tag. Together these factors can reduce power to localize the causal SNP by more than half. Other strategies commonly employed to increase power to detect association, namely increasing sample size and using higher density genotyping arrays, can, in certain common scenarios, actually exacerbate these effects and further decrease power to localize causal variants. We develop a re-ranking procedure that accounts for these adverse effects and substantially improves the accuracy of causal SNP identification, often doubling the probability that the causal SNP is top-ranked. Application to the NCI BPC3 aggressive prostate cancer GWAS with imputation meta-analysis identified a new top SNP at 2 of 3 associated loci and several additional possible causal SNPs at these loci that may have otherwise been overlooked. This method is simple to implement using R scripts provided on the author's website.
Cross-Population Joint Analysis of eQTLs: Fine Mapping and Functional Annotation  [PDF]
Xiaoquan Wen?,Francesca Luca?,Roger Pique-Regi
PLOS Genetics , 2015, DOI: 10.1371/journal.pgen.1005176
Abstract: Mapping expression quantitative trait loci (eQTLs) has been shown as a powerful tool to uncover the genetic underpinnings of many complex traits at molecular level. In this paper, we present an integrative analysis approach that leverages eQTL data collected from multiple population groups. In particular, our approach effectively identifies multiple independent cis-eQTL signals that are consistent across populations, accounting for population heterogeneity in allele frequencies and linkage disequilibrium patterns. Furthermore, by integrating genomic annotations, our analysis framework enables high-resolution functional analysis of eQTLs. We applied our statistical approach to analyze the GEUVADIS data consisting of samples from five population groups. From this analysis, we concluded that i) jointly analysis across population groups greatly improves the power of eQTL discovery and the resolution of fine mapping of causal eQTL ii) many genes harbor multiple independent eQTLs in their cis regions iii) genetic variants that disrupt transcription factor binding are significantly enriched in eQTLs (p-value = 4.93 × 10-22).
Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes  [cached]
Wu Chao,Zhu Jun,Zhang Xuegong
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-182
Abstract: Background To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods.
Association Testing of Clustered Rare Causal Variants in Case-Control Studies  [PDF]
Wan-Yu Lin
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0094337
Abstract: 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.
Bayesian Detection of Causal Rare Variants under Posterior Consistency  [PDF]
Faming Liang, Momiao Xiong
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0069633
Abstract: Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small--large- situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small--large- situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.
Systematic Fine-Mapping of Association with BMI and Type 2 Diabetes at the FTO Locus by Integrating Results from Multiple Ethnic Groups  [PDF]
Koichi Akiyama, Fumihiko Takeuchi, Masato Isono, Sureka Chakrawarthy, Quang Ngoc Nguyen, Wanqing Wen, Ken Yamamoto, Tomohiro Katsuya, Anuradhani Kasturiratne, Son Thai Pham, Wei Zheng, Yumi Matsushita, Miyako Kishimoto, Loi Doan Do, Xiao-Ou Shu, Ananda R. Wickremasinghe, Hiroshi Kajio, Norihiro Kato
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0101329
Abstract: Background/Objective The 16q12.2 locus in the first intron of FTO has been robustly associated with body mass index (BMI) and type 2 diabetes in genome-wide association studies (GWAS). To improve the resolution of fine-scale mapping at FTO, we performed a systematic approach consisting of two parts. Methods The first part is to partition the associated variants into linkage disequilibrium (LD) clusters, followed by conditional and haplotype analyses. The second part is to filter the list of potential causal variants through trans-ethnic comparison. Results We first examined the LD relationship between FTO SNPs showing significant association with type 2 diabetes in Japanese GWAS and between those previously reported in European GWAS. We could partition all the assayed or imputed SNPs showing significant association in the target FTO region into 7 LD clusters. Assaying 9 selected SNPs in 4 Asian-descent populations—Japanese, Vietnamese, Sri Lankan and Chinese (n≤26,109 for BMI association and n≤24,079 for type 2 diabetes association), we identified a responsible haplotype tagged by a cluster of SNPs and successfully narrowed the list of potential causal variants to 25 SNPs, which are the smallest in number among the studies conducted to date for FTO. Conclusions Our data support that the power to resolve the causal variants from those in strong LD increases consistently when three distant populations—Europeans, Asians and Africans—are included in the follow-up study. It has to be noted that this fine-mapping approach has the advantage of applicability to the existing GWAS data set in combination with direct genotyping of selected variants.
Integrating Probabilistic, Taxonomic and Causal Knowledge in Abductive Diagnosis  [PDF]
Dekang Lin,Randy Goebel
Computer Science , 2013,
Abstract: We propose an abductive diagnosis theory that integrates probabilistic, causal and taxonomic knowledge. Probabilistic knowledge allows us to select the most likely explanation; causal knowledge allows us to make reasonable independence assumptions; taxonomic knowledge allows causation to be modeled at different levels of detail, and allows observations be described in different levels of precision. Unlike most other approaches where a causal explanation is a hypothesis that one or more causative events occurred, we define an explanation of a set of observations to be an occurrence of a chain of causation events. These causation events constitute a scenario where all the observations are true. We show that the probabilities of the scenarios can be computed from the conditional probabilities of the causation events. Abductive reasoning is inherently complex even if only modest expressive power is allowed. However, our abduction algorithm is exponential only in the number of observations to be explained, and is polynomial in the size of the knowledge base. This contrasts with many other abduction procedures that are exponential in the size of the knowledge base.
Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression  [PDF]
Nanye Long ,Samuel P. Dickson,Jessica M. Maia,Hee Shin Kim,Qianqian Zhu,Andrew S. Allen
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003093
Abstract: 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.
Fine-Mapping the HOXB Region Detects Common Variants Tagging a Rare Coding Allele: Evidence for Synthetic Association in Prostate Cancer  [PDF]
Edward J. Saunders,Tokhir Dadaev,Daniel A. Leongamornlert,Sarah Jugurnauth-Little,Malgorzata Tymrakiewicz,Fredrik Wiklund,Ali Amin Al Olama,Sara Benlloch,David E. Neal equal contributor,Freddie C. Hamdy equal contributor,Jenny L. Donovan equal contributor,Graham G. Giles equal contributor,Gianluca Severi equal contributor,Henrik Gronberg equal contributor,Markus Aly equal contributor,Christopher A. Haiman equal contributor,Fredrick Schumacher equal contributor,Brian E. Henderson equal contributor,Sara Lindstrom equal contributor,Peter Kraft equal contributor,David J. Hunter equal contributor,Susan Gapstur equal contributor,Stephen Chanock equal contributor,Sonja I. Berndt equal contributor,Demetrius Albanes equal contributor,Gerald Andriole equal contributor,Johanna Schleutker equal contributor,Maren Weischer equal contributor,B?rge G. Nordestgaard equal contributor,Federico Canzian equal contributor,Daniele Campa equal contributor,Elio Riboli equal contributor,Tim J. Key equal contributor,Ruth C. Travis equal contributor,Sue A. Ingles equal contributor,Esther M. John equal contributor,Richard B. Hayes equal contributor,Paul Pharoah equal contributor,Kay-Tee Khaw equal contributor,Janet L. Stanford equal contributor,Elaine A. Ostrander equal contributor,Lisa B. Signorello equal contributor,Stephen N. Thibodeau equal contributor,Daniel Schaid equal contributor,Christiane Maier equal contributor,Adam S. Kibel equal contributor,Cezary Cybulski equal contributor
PLOS Genetics , 2014, DOI: doi/10.1371/journal.pgen.1004129
Abstract: 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.
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