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1.  Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence 
Bioinformatics  2008;24(16):1805-1811.
Motivation: A challenging problem after a genome-wide association study (GWAS) is to balance the statistical evidence of genotype–phenotype correlation with a priori evidence of biological relevance.
Results: We introduce a method for systematically prioritizing single nucleotide polymorphisms (SNPs) for further study after a GWAS. The method combines evidence across multiple domains including statistical evidence of genotype–phenotype correlation, known pathways in the pathologic development of disease, SNP/gene functional properties, comparative genomics, prior evidence of genetic linkage, and linkage disequilibrium. We apply this method to a GWAS of nicotine dependence, and use simulated data to test it on several commercial SNP microarrays.
Availability: A comprehensive database of biological prioritization scores for all known SNPs is available at This can be used to prioritize nicotine dependence association studies through a straightforward mathematical formula—no special software is necessary.
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2610477  PMID: 18565990
2.  Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations 
PLoS Genetics  2010;6(4):e1000895.
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.
Author Summary
Genome-wide association studies have led to the identification of susceptibility loci for a variety of human complex traits. What is still largely missing, however, is the understanding of the biological context in which these candidate variants act and of how they determine each trait. Given the localization of many GWAS loci outside coding regions and the important role of regulatory variation in shaping phenotypic variance, gene expression has been proposed as a plausible informative intermediate phenotype. Here we show that for a subset of the currently published GWAS this is indeed the case, by observing a significant excess of regulatory variants among disease loci. We propose an empirical methodology (regulatory trait concordance—RTC) able to integrate expression and disease data in order to detect causal regulatory effects. We show that the RTC outperforms simple correlation metrics under various simulated linkage disequilibrium (LD) scenarios. Our method is able to recover previously suspected causal regulatory effects from the literature and, as expected given the nature of the tested tissue, an overrepresentation of immunity-related candidates is observed. As the number of available tissues will increase, this prioritization approach will become even more useful in understanding the implication of regulatory variants in disease etiology.
PMCID: PMC2848550  PMID: 20369022
3.  Convergence of genetic influences in comorbidity 
BMC Bioinformatics  2012;13(Suppl 2):S8.
Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data.
We modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data.
After validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants.
ModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.
PMCID: PMC3375629  PMID: 22536871
4.  A noise-reduction GWAS analysis implicates altered regulation of neurite outgrowth and guidance in autism 
Molecular Autism  2011;2:1.
Genome-wide Association Studies (GWAS) have proved invaluable for the identification of disease susceptibility genes. However, the prioritization of candidate genes and regions for follow-up studies often proves difficult due to false-positive associations caused by statistical noise and multiple-testing. In order to address this issue, we propose the novel GWAS noise reduction (GWAS-NR) method as a way to increase the power to detect true associations in GWAS, particularly in complex diseases such as autism.
GWAS-NR utilizes a linear filter to identify genomic regions demonstrating correlation among association signals in multiple datasets. We used computer simulations to assess the ability of GWAS-NR to detect association against the commonly used joint analysis and Fisher's methods. Furthermore, we applied GWAS-NR to a family-based autism GWAS of 597 families and a second existing autism GWAS of 696 families from the Autism Genetic Resource Exchange (AGRE) to arrive at a compendium of autism candidate genes. These genes were manually annotated and classified by a literature review and functional grouping in order to reveal biological pathways which might contribute to autism aetiology.
Computer simulations indicate that GWAS-NR achieves a significantly higher classification rate for true positive association signals than either the joint analysis or Fisher's methods and that it can also achieve this when there is imperfect marker overlap across datasets or when the closest disease-related polymorphism is not directly typed. In two autism datasets, GWAS-NR analysis resulted in 1535 significant linkage disequilibrium (LD) blocks overlapping 431 unique reference sequencing (RefSeq) genes. Moreover, we identified the nearest RefSeq gene to the non-gene overlapping LD blocks, producing a final candidate set of 860 genes. Functional categorization of these implicated genes indicates that a significant proportion of them cooperate in a coherent pathway that regulates the directional protrusion of axons and dendrites to their appropriate synaptic targets.
As statistical noise is likely to particularly affect studies of complex disorders, where genetic heterogeneity or interaction between genes may confound the ability to detect association, GWAS-NR offers a powerful method for prioritizing regions for follow-up studies. Applying this method to autism datasets, GWAS-NR analysis indicates that a large subset of genes involved in the outgrowth and guidance of axons and dendrites is implicated in the aetiology of autism.
PMCID: PMC3035032  PMID: 21247446
5.  In search of causal variants: refining disease association signals using cross-population contrasts 
BMC Genetics  2008;9:58.
Genome-wide association (GWA) using large numbers of single nucleotide polymorphisms (SNPs) is now a powerful, state-of-the-art approach to mapping human disease genes. When a GWA study detects association between a SNP and the disease, this signal usually represents association with a set of several highly correlated SNPs in strong linkage disequilibrium. The challenge we address is to distinguish among these correlated loci to highlight potential functional variants and prioritize them for follow-up.
We implemented a systematic method for testing association across diverse population samples having differing histories and LD patterns, using a logistic regression framework. The hypothesis is that important underlying biological mechanisms are shared across human populations, and we can filter correlated variants by testing for heterogeneity of genetic effects in different population samples. This approach formalizes the descriptive comparison of p-values that has typified similar cross-population fine-mapping studies to date. We applied this method to correlated SNPs in the cholinergic nicotinic receptor gene cluster CHRNA5-CHRNA3-CHRNB4, in a case-control study of cocaine dependence composed of 504 European-American and 583 African-American samples. Of the 10 SNPs genotyped in the r2 ≥ 0.8 bin for rs16969968, three demonstrated significant cross-population heterogeneity and are filtered from priority follow-up; the remaining SNPs include rs16969968 (heterogeneity p = 0.75). Though the power to filter out rs16969968 is reduced due to the difference in allele frequency in the two groups, the results nevertheless focus attention on a smaller group of SNPs that includes the non-synonymous SNP rs16969968, which retains a similar effect size (odds ratio) across both population samples.
Filtering out SNPs that demonstrate cross-population heterogeneity enriches for variants more likely to be important and causative. Our approach provides an important and effective tool to help interpret results from the many GWA studies now underway.
PMCID: PMC2556340  PMID: 18759969
6.  GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation 
PLoS Genetics  2014;10(11):e1004787.
Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at
Author Summary
In the past 10 years, many genome wide association studies (GWAS) have been conducted to identify the genetic bases of complex human traits. As of January, 2014, more than 12,000 single-nucleotide polymorphisms (SNPs) have been reported to be significantly associated with at least one complex trait/disease. On one hand, about 85% of identified risk variants are located in non-coding regions, which motivates a systematic understanding of the function of non-coding variants in regulatory elements in the human genome. On the other hand, complex diseases are often affected by many genetic variants with small or moderate effects. To address these issues, we propose a statistical approach, GPA, to integrating information from multiple GWAS datasets and functional annotation. Notably, our approach only requires marker-wise p-values as input, making it especially useful when only summary statistics, instead of the full genotype and phenotype data, are available. We applied GPA to analyze GWAS datasets of five psychiatric disorders and bladder cancer, where the central nervous system genes, eQTLs from the Genotype-Tissue Expression (GTEx), and the ENCODE DNase-seq data from 125 cell lines were used as functional annotation. The analysis results suggest that GPA is an effective method for integrative data analysis in the post-GWAS era.
PMCID: PMC4230845  PMID: 25393678
7.  Replication of genetic loci for ages at menarche and menopause in the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) study 
Human Reproduction (Oxford, England)  2013;28(6):1695-1706.
Do genetic associations identified in genome-wide association studies (GWAS) of age at menarche (AM) and age at natural menopause (ANM) replicate in women of diverse race/ancestry from the Population Architecture using Genomics and Epidemiology (PAGE) Study?
We replicated GWAS reproductive trait single nucleotide polymorphisms (SNPs) in our European descent population and found that many SNPs were also associated with AM and ANM in populations of diverse ancestry.
Menarche and menopause mark the reproductive lifespan in women and are important risk factors for chronic diseases including obesity, cardiovascular disease and cancer. Both events are believed to be influenced by environmental and genetic factors, and vary in populations differing by genetic ancestry and geography. Most genetic variants associated with these traits have been identified in GWAS of European-descent populations.
A total of 42 251 women of diverse ancestry from PAGE were included in cross-sectional analyses of AM and ANM.
SNPs previously associated with ANM (n = 5 SNPs) and AM (n = 3 SNPs) in GWAS were genotyped in American Indians, African Americans, Asians, European Americans, Hispanics and Native Hawaiians. To test SNP associations with ANM or AM, we used linear regression models stratified by race/ethnicity and PAGE sub-study. Results were then combined in race-specific fixed effect meta-analyses for each outcome. For replication and generalization analyses, significance was defined at P < 0.01 for ANM analyses and P < 0.017 for AM analyses.
We replicated findings for AM SNPs in the LIN28B locus and an intergenic region on 9q31 in European Americans. The LIN28B SNPs (rs314277 and rs314280) were also significantly associated with AM in Asians, but not in other race/ethnicity groups. Linkage disequilibrium (LD) patterns at this locus varied widely among the ancestral groups. With the exception of an intergenic SNP at 13q34, all ANM SNPs replicated in European Americans. Three were significantly associated with ANM in other race/ethnicity populations: rs2153157 (6p24.2/SYCP2L), rs365132 (5q35/UIMC1) and rs16991615 (20p12.3/MCM8). While rs1172822 (19q13/BRSK1) was not significant in the populations of non-European descent, effect sizes showed similar trends.
Lack of association for the GWAS SNPs in the non-European American groups may be due to differences in locus LD patterns between these groups and the European-descent populations included in the GWAS discovery studies; and in some cases, lower power may also contribute to non-significant findings.
The discovery of genetic variants associated with the reproductive traits provides an important opportunity to elucidate the biological mechanisms involved with normal variation and disorders of menarche and menopause. In this study we replicated most, but not all reported SNPs in European descent populations and examined the epidemiologic architecture of these early reported variants, describing their generalizability and effect size across differing ancestral populations. Such data will be increasingly important for prioritizing GWAS SNPs for follow-up in fine-mapping and resequencing studies, as well as in translational research.
The Population Architecture Using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute (NHGRI), supported by U01HG004803 (CALiCo), U01HG004798 (EAGLE), U01HG004802 (MEC), U01HG004790 (WHI) and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. The authors report no conflicts of interest.
PMCID: PMC3657124  PMID: 23508249
menopause; menarche; genome-wide association study; race/ethnicity; single nucleotide polymorphism
8.  Re-Ranking Sequencing Variants in the Post-GWAS Era for Accurate Causal Variant Identification 
PLoS Genetics  2013;9(8):e1003609.
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.
Author Summary
As next-generation sequencing (NGS) costs continue to fall and genome-wide association study (GWAS) platform coverage improves, the human genetics community is positioned to identify potentially causal variants. However, current NGS or imputation-based studies of either the whole genome or regions previously identified by GWAS have not yet been very successful in identifying causal variants. A major hurdle is the development of methods to distinguish disease-causing variants from their highly-correlated proxies within an associated region. We show that various common factors, such as differential sequencing or imputation accuracy rates and linkage disequilibrium patterns, with or without GWAS-informed region selection, can substantially decrease the probability of identifying the correct causal SNP, often by more than half. We then describe a novel and easy-to-implement re-ranking procedure that can double the probability that the causal SNP is top-ranked in many settings. Application to the NCI Breast and Prostate Cancer (BPC3) Cohort Consortium aggressive prostate cancer data identified new top SNPs within two associated loci previously established via GWAS, as well as several additional possible causal SNPs that had been previously overlooked.
PMCID: PMC3738448  PMID: 23950724
9.  SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study 
Nucleic Acids Research  2010;38(Web Server issue):W201-W209.
SPOT (, the SNP prioritization online tool, is a web site for integrating biological databases into the prioritization of single nucleotide polymorphisms (SNPs) for further study after a genome-wide association study (GWAS). Typically, the next step after a GWAS is to genotype the top signals in an independent replication sample. Investigators will often incorporate information from biological databases so that biologically relevant SNPs, such as those in genes related to the phenotype or with potentially non-neutral effects on gene expression such as a splice sites, are given higher priority. We recently introduced the genomic information network (GIN) method for systematically implementing this kind of strategy. The SPOT web site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the GIN method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.
PMCID: PMC2896195  PMID: 20529875
10.  SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS 
BMC Bioinformatics  2013;14(Suppl 1):S9.
The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.
We propose a new bioinformatics approach to support biological data mining in the analysis and interpretation of SNPs associated to pathologies. This system can be employed to design custom genotyping chips for disease-oriented studies and to re-score GWAS results. The proposed method relies (1) on the data integration of public resources using a gene-centric database design, (2) on the evaluation of a set of static biomolecular annotations, defined as features, and (3) on the SNP scoring function, which computes SNP scores using parameters and weights set by users. We employed a machine learning classifier to set default feature weights and an ontological annotation layer to enable the enrichment of the input gene set. We implemented our method as a web tool called SNPranker 2.0 (, improving our first published release of this system. A user-friendly interface allows the input of a list of genes, SNPs or a biological process, and to customize the features set with relative weights. As result, SNPranker 2.0 returns a list of SNPs, localized within input and ontologically enriched genes, combined with their prioritization scores.
Different databases and resources are already available for SNPs annotation, but they do not prioritize or re-score SNPs relying on a-priori biomolecular knowledge. SNPranker 2.0 attempts to fill this gap through a user-friendly integrated web resource. End users, such as researchers in medical genetics and epidemiology, may find in SNPranker 2.0 a new tool for data mining and interpretation able to support SNPs analysis. Possible scenarios are GWAS data re-scoring, SNPs selection for custom genotyping arrays and SNPs/diseases association studies.
PMCID: PMC3548692  PMID: 23369106
11.  Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies 
PLoS Genetics  2014;10(10):e1004722.
Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.
Author Summary
Genome-wide association studies (GWAS) have successfully identified numerous regions in the genome that harbor genetic variants that increase risk for various complex traits and diseases. However, it is generally the case that GWAS risk variants are not themselves causally affecting the trait, but rather, are correlated to the true causal variant through linkage disequilibrium (LD). Plausible causal variants are identified in fine-mapping studies through targeted sequencing followed by prioritization of variants for functional validation. In this work, we propose methods that leverage two sources of independent information, the association strength and genomic functional location, to prioritize causal variants. We demonstrate in simulations and empirical data that our approach reduces the number of SNPs that need to be selected for follow-up to identify the true causal variants at GWAS risk loci.
PMCID: PMC4214605  PMID: 25357204
12.  Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies 
The apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.
Phenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin.
Fitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies.
The results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.
PMCID: PMC4176739  PMID: 24168700
13.  Genome-wide association study combined with biological context can reveal more disease-related SNPs altering microRNA target seed sites 
BMC Genomics  2014;15(1):669.
Emerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3′UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs.
In 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10−5, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively).
MRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.
Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-669) contains supplementary material, which is available to authorized users.
PMCID: PMC4246476  PMID: 25106527
microRNA; Genome-wide association study; Single nucleotide polymorphisms; Human diseases and cancers
14.  Systematic permutation testing in GWAS pathway analyses: identification of genetic networks in dilated cardiomyopathy and ulcerative colitis 
BMC Genomics  2014;15:622.
Genome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases. Analogous to the evolution of gene expression analyses, pathway analyses have emerged as important tools to uncover functional networks of genome-wide association data. Usually, pathway analyses combine statistical methods with a priori available biological knowledge. To determine significance thresholds for associated pathways, correction for multiple testing and over-representation permutation testing is applied.
We systematically investigated the impact of three different permutation test approaches for over-representation analysis to detect false positive pathway candidates and evaluate them on genome-wide association data of Dilated Cardiomyopathy (DCM) and Ulcerative Colitis (UC). Our results provide evidence that the gold standard - permuting the case–control status – effectively improves specificity of GWAS pathway analysis. Although permutation of SNPs does not maintain linkage disequilibrium (LD), these permutations represent an alternative for GWAS data when case–control permutations are not possible. Gene permutations, however, did not add significantly to the specificity. Finally, we provide estimates on the required number of permutations for the investigated approaches.
To discover potential false positive functional pathway candidates and to support the results from standard statistical tests such as the Hypergeometric test, permutation tests of case control data should be carried out. The most reasonable alternative was case–control permutation, if this is not possible, SNP permutations may be carried out. Our study also demonstrates that significance values converge rapidly with an increasing number of permutations. By applying the described statistical framework we were able to discover axon guidance, focal adhesion and calcium signaling as important DCM-related pathways and Intestinal immune network for IgA production as most significant UC pathway.
PMCID: PMC4223581  PMID: 25052024
DCM; UC; GWAS; Permutation tests; Pathway analysis
15.  GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm 
PLoS Genetics  2013;9(8):e1003657.
Genome-wide association studies (GWAS) yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s)-trait(s) associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS) to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite the relatively small size of GHS (n = 3,175), when compared with the largest published meta-GWAS (n>100,000), GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify associated variants. This provides a powerful tool for the analysis of diverse genomic features, for instance including gene expression and exome sequencing data, where complex dependencies are present in the predictor space.
Author Summary
Nowadays, the availability of cheaper and accurate assays to quantify multiple (endo)phenotypes in large population cohorts allows multi-trait studies. However, these studies are limited by the lack of flexible models integrated with efficient computational tools for genome-wide multi SNPs-traits analyses. To overcome this problem, we propose a novel Bayesian analysis strategy and a new algorithmic implementation which exploits parallel processing architecture for fully multivariate modeling of groups of correlated phenotypes at the genome-wide scale. In addition to increased power of our algorithm over alternative Bayesian and well-established non-Bayesian multi-phenotype methods, we provide an application to a real case study of several blood lipid traits, and show how our method recovered most of the major associations and is better at refining multi-trait polygenic associations than alternative methods. We reveal and replicate in independent cohorts new associations with two phenotypic groups that were not detected by competing multivariate approaches and not noticed by a large meta-GWAS. We also discuss the applicability of the proposed method to large meta-analyses involving hundreds of thousands of individuals and to diverse genomic datasets where complex dependencies in the predictor space are present.
PMCID: PMC3738451  PMID: 23950726
16.  Prioritization of SNPs for Genome-Wide Association Studies Using an Interaction Model of Genetic Variation, Gene Expression, and Trait Variation 
Molecules and Cells  2012;33(4):351-361.
The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in drug-induced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.
PMCID: PMC3887803  PMID: 22460606
genome-wide association study; interaction network; prioritization; SNP
17.  Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data 
BMC Systems Biology  2014;8(Suppl 2):S5.
High-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.
We developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.
We developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.
PMCID: PMC4101698  PMID: 25032480
18.  Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data 
Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.
We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.
A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.
We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.
PMCID: PMC3618247  PMID: 23566118
19.  Enhanced Statistical Tests for GWAS in Admixed Populations: Assessment using African Americans from CARe and a Breast Cancer Consortium 
PLoS Genetics  2011;7(4):e1001371.
While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.
Author Summary
This paper presents improved methodologies for the analysis of genome-wide association studies in admixed populations, which are populations that came about by the mixing of two or more distant continental populations over a few hundred years (e.g., African Americans or Latinos). Studies of admixed populations offer the promise of capturing additional genetic diversity compared to studies over homogeneous populations such as Europeans. In admixed populations, correlation between genetic variants exists both at a fine scale in the ancestral populations and at a coarse scale due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered either one or the other type of correlation, but not both. In this work we develop novel statistical methods that account for both types of genetic correlation, and we show that the combined approach attains greater statistical power than that achieved by applying either approach separately. We provide analysis of simulated and real data from major studies performed in African-American men and women to show the improvement obtained by our methods over the standard methods for analyzing association studies in admixed populations.
PMCID: PMC3080860  PMID: 21541012
20.  On the identification of potential regulatory variants within genome wide association candidate SNP sets 
BMC Medical Genomics  2014;7:34.
Genome wide association studies (GWAS) are a population-scale approach to the identification of segments of the genome in which genetic variations may contribute to disease risk. Current methods focus on the discovery of single nucleotide polymorphisms (SNPs) associated with disease traits. As there are many SNPs within identified risk loci, and the majority of these are situated within non-coding regions, a key challenge is to identify and prioritize variants affecting regulatory sequences that are likely to contribute to the phenotype assessed.
We focused investigation on SNPs within lung and breast cancer GWAS loci that reached genome-wide significance for potential roles in gene regulation with a specific focus on SNPs likely to disrupt transcription factor binding sites. Within risk loci, the regulatory potential of sub-regions was classified using relevant open chromatin and epigenetic high throughput sequencing data sets from the ENCODE project in available cancer and normal cell lines. Furthermore, transcription factor affinity altering variants were predicted by comparison of position weight matrix scores between disease and reference alleles. Lastly, ChIP-seq data of transcription associated factors and topological domains were included as binding evidence and potential gene target inference.
The sets of SNPs, including both the disease-associated markers and those in high linkage disequilibrium with them, were significantly over-represented in regulatory sequences of cancer and/or normal cells; however, over-representation was generally not restricted to disease-relevant tissue specific regions. The calculated regulatory potential, allelic binding affinity scores and ChIP-seq binding evidence were the three criteria used to prioritize candidates. Fitting all three criteria, we highlighted breast cancer susceptibility SNPs and a borderline lung cancer relevant SNP located in cancer-specific enhancers overlapping multiple distinct transcription associated factor ChIP-seq binding sites.
Incorporating high throughput sequencing epigenetic and transcription factor data sets from both cancer and normal cells into cancer genetic studies reveals potential functional SNPs and informs subsequent characterization efforts.
PMCID: PMC4066296  PMID: 24920305
GWAS; Lung cancer; Regulatory regions; Gene regulation; Transcription factor binding site alteration; Enhancer; Topological domains
21.  Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network, and pathway analyses 
Frontiers in Genetics  2014;5:214.
Obesity is a complex condition with world-wide exponentially rising prevalence rates, linked with severe diseases like Type 2 Diabetes. Economic and welfare consequences have led to a raised interest in a better understanding of the biological and genetic background. To date, whole genome investigations focusing on single genetic variants have achieved limited success, and the importance of including genetic interactions is becoming evident. Here, the aim was to perform an integrative genomic analysis in an F2 pig resource population that was constructed with an aim to maximize genetic variation of obesity-related phenotypes and genotyped using the 60K SNP chip. Firstly, Genome Wide Association (GWA) analysis was performed on the Obesity Index to locate candidate genomic regions that were further validated using combined Linkage Disequilibrium Linkage Analysis and investigated by evaluation of haplotype blocks. We built Weighted Interaction SNP Hub (WISH) and differentially wired (DW) networks using genotypic correlations amongst obesity-associated SNPs resulting from GWA analysis. GWA results and SNP modules detected by WISH and DW analyses were further investigated by functional enrichment analyses. The functional annotation of SNPs revealed several genes associated with obesity, e.g., NPC2 and OR4D10. Moreover, gene enrichment analyses identified several significantly associated pathways, over and above the GWA study results, that may influence obesity and obesity related diseases, e.g., metabolic processes. WISH networks based on genotypic correlations allowed further identification of various gene ontology terms and pathways related to obesity and related traits, which were not identified by the GWA study. In conclusion, this is the first study to develop a (genetic) obesity index and employ systems genetics in a porcine model to provide important insights into the complex genetic architecture associated with obesity and many biological pathways that underlie it.
PMCID: PMC4087325  PMID: 25071839
obesity index; animal model; high-throughput genotype data; systems genetics; WISH network
22.  Development and Evaluation of a Genetic Risk Score for Obesity 
Biodemography and social biology  2013;59(1):10.1080/19485565.2013.774628.
Results from genome-wide association studies (GWAS) represent a potential resource for etiological and treatment research. GWAS of obesity-related phenotypes have been especially successful. To translate this success into a research tool, we developed and tested a “genetic risk score” (GRS) that summarizes an individual’s genetic predisposition to obesity.
Different GWAS of obesity-related phenotypes report different sets of single nucleotide polymorphisms (SNPs) as the best genomic markers of obesity risk. Therefore, we applied a 3-stage approach that pooled results from multiple GWAS to select SNPs to include in our GRS: The 3 stages are (1) Extraction. SNPs with evidence of association are compiled from published GWAS; (2) Clustering. SNPs are grouped according to patterns of linkage disequilibrium; (3) Selection. Tag SNPs are selected from clusters that meet specific criteria. We applied this 3-stage approach to results from 16 GWAS of obesity-related phenotypes in European-descent samples to create a GRS. We then tested the GRS in the Atherosclerosis Risk in the Communities (ARIC) Study cohort (N=10,745, 55% female, 77% white, 23% African American).
Our 32-locus GRS was a statistically significant predictor of body mass index (BMI) and obesity among ARIC whites (for BMI, r=0.13, p<1×10−30; for obesity, area under the receiver operating characteristic curve (AUC)=0.57 [95% CI 0.55–0.58]). The GRS improved prediction of obesity (as measured by delta-AUC and integrated discrimination index) when added to models that included demographic and geographic information. FTO- and MC4R-linked SNPs, and a non-genetic risk assessment consisting of a socioeconomic index (p<0.01 for all comparisons). The GRS also predicted increased mortality risk over 17 years of follow-up. The GRS performed less well among African Americans.
The obesity GRS derived using our 3-stage approach is not useful for clinical risk prediction, but may have value as a tool for etiological and treatment research.
PMCID: PMC3671353  PMID: 23701538
23.  Snat: a SNP annotation tool for bovine by integrating various sources of genomic information 
BMC Genetics  2011;12:85.
Most recently, with maturing of bovine genome sequencing and high throughput SNP genotyping technologies, a large number of significant SNPs associated with economic important traits can be identified by genome-wide association studies (GWAS). To further determine true association findings in GWAS, the common strategy is to sift out most promising SNPs for follow-up replication studies. Hence it is crucial to explore the functional significance of the candidate SNPs in order to screen and select the potential functional ones. To systematically prioritize these statistically significant SNPs and facilitate follow-up replication studies, we developed a bovine SNP annotation tool (Snat) based on a web interface.
With Snat, various sources of genomic information are integrated and retrieved from several leading online databases, including SNP information from dbSNP, gene information from Entrez Gene, protein features from UniProt, linkage information from AnimalQTLdb, conserved elements from UCSC Genome Browser Database and gene functions from Gene Ontology (GO), KEGG PATHWAY and Online Mendelian Inheritance in Animals (OMIA). Snat provides two different applications, including a CGI-based web utility and a command-line version, to access the integrated database, target any single nucleotide loci of interest and perform multi-level functional annotations. For further validation of the practical significance of our study, SNPs involved in two commercial bovine SNP chips, i.e., the Affymetrix Bovine 10K chip array and the Illumina 50K chip array, have been annotated by Snat, and the corresponding outputs can be directly downloaded from Snat website. Furthermore, a real dataset involving 20 identified SNPs associated with milk yield in our recent GWAS was employed to demonstrate the practical significance of Snat.
To our best knowledge, Snat is one of first tools focusing on SNP annotation for livestock. Snat confers researchers with a convenient and powerful platform to aid functional analyses and accurate evaluation on genes/variants related to SNPs, and facilitates follow-up replication studies in the post-GWAS era.
PMCID: PMC3224132  PMID: 21982513
24.  Genetic variants associated with idiopathic pulmonary fibrosis susceptibility and mortality: a genome-wide association study 
The lancet. Respiratory medicine  2013;1(4):309-317.
Idiopathic pulmonary fibrosis (IPF) is a devastating disease that probably involves several genetic loci. Several rare genetic variants and one common single nucleotide polymorphism (SNP) of MUC5B have been associated with the disease. Our aim was to identify additional common variants associated with susceptibility and ultimately mortality in IPF.
First, we did a three-stage genome-wide association study (GWAS): stage one was a discovery GWAS; and stages two and three were independent case-control studies. DNA samples from European-American patients with IPF meeting standard criteria were obtained from several US centres for each stage. Data for European-American control individuals for stage one were gathered from the database of genotypes and phenotypes; additional control individuals were recruited at the University of Pittsburgh to increase the number. For controls in stages two and three, we gathered data for additional sex-matched European-American control individuals who had been recruited in another study. DNA samples from patients and from control individuals were genotyped to identify SNPs associated with IPF. SNPs identified in stage one were carried forward to stage two, and those that achieved genome-wide significance (p<5 × 10−8) in a meta-analysis were carried forward to stage three. Three case series with follow-up data were selected from stages one and two of the GWAS using samples with follow-up data. Mortality analyses were done in these case series to assess the SNPs associated with IPF that had achieved genome-wide significance in the meta-analysis of stages one and two. Finally, we obtained gene-expression profiling data for lungs of patients with IPF from the Lung Genomics Research Consortium and analysed correlation with SNP genotypes.
In stage one of the GWAS (542 patients with IPF, 542 control individuals matched one-by-one to cases by genetic ancestry estimates), we identified 20 loci. Six SNPs reached genome-wide significance in stage two (544 patients, 687 control individuals): three TOLLIP SNPs (rs111521887, rs5743894, rs5743890) and one MUC5B SNP (rs35705950) at 11p15.5; one MDGA2 SNP (rs7144383) at 14q21.3; and one SPPL2C SNP (rs17690703) at 17q21.31. Stage three (324 patients, 702 control individuals) confirmed the associations for all these SNPs, except for rs7144383. Linkage disequilibrium between the MUC5B SNP (rs35705950) and TOLLIP SNPs (rs111521887 [r2=0.07], rs5743894 [r2=0.16], and rs5743890 [r2=0.01]) was low. 683 patients from the GWAS were included in the mortality analysis. Individuals who developed IPF despite having the protective TOLLIP minor allele of rs5743890 carried an increased mortality risk (meta-analysis with fixed-effect model: hazard ratio 1.72 [95% CI 1.24–2.38]; p=0.0012). TOLLIP expression was decreased by 20% in individuals carrying the minor allele of rs5743890 (p=0.097), 40% in those with the minor allele of rs111521887 (p=3.0 × 10−4), and 50% in those with the minor allele of rs5743894 (p=2.93 × 10−5) compared with homozygous carriers of common alleles for these SNPs.
Novel variants in TOLLIP and SPPL2C are associated with IPF susceptibility. One novel variant of TOLLIP, rs5743890, is also associated with mortality. These associations and the reduced expression of TOLLIP in patients with IPF who carry TOLLIP SNPs emphasise the importance of this gene in the disease.
National Institutes of Health; National Heart, Lung, and Blood Institute; Pulmonary Fibrosis Foundation; Coalition for Pulmonary Fibrosis; and Instituto de Salud Carlos III.
PMCID: PMC3894577  PMID: 24429156
25.  iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies 
BMC Genomics  2012;13(Suppl 7):S2.
Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).
In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.
iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at
PMCID: PMC3521387  PMID: 23281813

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