Etiology Of Complex Diseases Research Articles

On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies

BackgroundGenome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain.ResultsHere we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.ConclusionsWe show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.

A novel method to identify high order gene-gene interactions in genome-wide association studies: Gene-based MDR

BackgroundBecause common complex diseases are affected by multiple genes and environmental factors, it is essential to investigate gene-gene and/or gene-environment interactions to understand genetic architecture of complex diseases. After the great success of large scale genome-wide association (GWA) studies using the high density single nucleotide polymorphism (SNP) chips, the study of gene-gene interaction becomes a next challenge. Multifactor dimensionality reduction (MDR) analysis has been widely used for the gene-gene interaction analysis. In practice, however, it is not easy to perform high order gene-gene interaction analyses via MDR in genome-wide level because it requires exploring a huge search space and suffers from a computational burden due to high dimensionality.ResultsWe propose dimensional reduction analysis, Gene-MDR analysis for the fast and efficient high order gene-gene interaction analysis. The proposed Gene-MDR method is composed of two-step applications of MDR: within- and between-gene MDR analyses. First, within-gene MDR analysis summarizes each gene effect via MDR analysis by combining multiple SNPs from the same gene. Second, between-gene MDR analysis then performs interaction analysis using the summarized gene effects from within-gene MDR analysis. We apply the Gene-MDR method to bipolar disorder (BD) GWA data from Wellcome Trust Case Control Consortium (WTCCC). The results demonstrate that Gene-MDR is capable of detecting high order gene-gene interactions associated with BD.ConclusionBy reducing the dimension of genome-wide data from SNP level to gene level, Gene-MDR efficiently identifies high order gene-gene interactions. Therefore, Gene-MDR can provide the key to understand complex disease etiology.

Comprehensive Analysis of Copy Number Variation of Genes at Chromosome 1 and 10 Loci Associated with Late Age Related Macular Degeneration

Copy Number Variants (CNVs) are now recognized as playing a significant role in complex disease etiology. Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss in the western world. While a number of genes and environmental factors have been associated with both risk and protection in AMD, the role of CNVs has remained largely unexplored. We analyzed the two major AMD risk-associated regions on chromosome 1q32 and 10q26 for CNVs using Multiplex Ligation-dependant Probe Amplification. The analysis targeted nine genes in these two key regions, including the Complement Factor H (CFH) gene, the 5 CFH-related (CFHR) genes representing a known copy number “hotspot”, the F13B gene as well as the ARMS2 and HTRA1 genes in 387 cases of late AMD and 327 controls. No copy number variation was detected at the ARMS2 and HTRA1 genes in the chromosome 10 region, nor for the CFH and F13B genes at the chromosome 1 region. However, significant association was identified for the CFHR3-1 deletion in AMD cases (p = 2.38×10−12) OR = 0.31, CI-0.95 (0.23–0.44), for both neovascular disease (nAMD) (p = 8.3×10−9) OR = 0.36 CI-0.95 (0.25–0.52) and geographic atrophy (GA) (p = 1.5×10−6) OR = 0.36 CI-0.95 (0.25–0.52) compared to controls. In addition, a significant association with deletion of CFHR1-4 was identified only in patients who presented with bilateral GA (p = 0.02) (OR = 7.6 CI-0.95 1.38–41.8). This is the first report of a phenotype specific association of a CNV for a major subtype of AMD and potentially allows for pre-diagnostic identification of individuals most likely to proceed to this end stage of disease.

Abstract 5008: Genome-wide methylation and ovarian cancer survival

Abstract Background Ovarian cancer is the fifth most common cause of cancer death in women in the US; approximately 15,000 women will die from this disease in 2011 (ACS, 2011). The 5 year survival rate for stage IV disease is only 18%. Only a handful of independent significant prognostic factors are known, including tumor stage at surgery, size of residual tumor at the end of primary surgery, and age at diagnosis. Understanding how the variation of cytosine methylation in the context of cytosine-guanine dinucleotides (CpGs) affects complex disease etiology and survival is the current focus of many ongoing research studies. We conducted an epigenome-wide study of methylation in ovarian cancer cases to evaluate whether variation in methylation of CpG sites in genomic DNA may be associated with ovarian cancer survival. Methods Subjects included cases from an ongoing case-control study. The Mayo Clinic Ovarian Cancer Study is a clinic-based study that recruits ovarian cancer cases within 12 months of diagnosis. Pre-treatment, germline DNA from lymphocytes from 137 cases was bisulfite-modified, amplified and genotyped on the Illumina 450K methylation chip and included in these analyses. Quality control (QC) analyses include the elimination of all methylation probes with a single nucleotide polymorphism (SNP) or insertion/deletion (in/del) at the same location; removal of non-specific probes; and estimation of bisulfite conversion rates using embedded control probes. Planned analyses include assessment of clinical covariates (age, stage, extent of surgical debulking, grade, presence of ascites) to determine which are associated with overall survival in our data. Additional analyses will assess the age- and multi-variate-adjusted association of each methylation probe with overall survival using Cox proportional hazards regression models with each methylation probe modeled as a continuous variable. Data will be summarized using Manhattan plots. These analyses will be repeated using ‘time to recurrence’ as the outcome variable, with recurrence defined as time to ovarian cancer recurrence/progression or death. Results Bioinformatics analyses suggest that 36,659 probes were located at the site of a SNP or in/del and should be removed from future analyses. Another 448 probes had a detection rate of p< 0.01 and 6630 probes had signals for the negative control samples; all were removed from analyses. A total of 43,802 probes were removed. In addition, ten cases were excluded due to either poor bisulfite conversion (N=9) or extremely low raw methylation values across all probes (N=1). This leaves a total of 127 cases for analyses. Results from planned analyses will be presented, including estimation of the association of methylation probes with both overall survival and time to recurrence. Conclusions Data to be presented will describe whether there is any evidence that methylation of genomic DNA in ovarian cancer cases is associated with outcome of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5008. doi:1538-7445.AM2012-5008

Genome-wide association study of skin complex diseases

Complex diseases are caused by both genetic and environmental factors. Over decades, scientists endeavored to uncover the genetic myth of complex diseases by linkage and association studies. Since 2005, the genome-wide association study (GWAS) has been proved to be the most powerful and efficient study design thus far in identifying genetic variants that are associated with complex diseases. More than 230 complex diseases and traits have been investigated by this approach. In dermatology, 10 skin complex diseases have been investigated, a wealth of common susceptibility variants conferring risk for skin complex diseases have been discovered. These findings point to genes and/or loci involved in biological systems worth further investigating by using other methodologies. Certainly, as our understanding of the genetic etiology of skin complex diseases continues to mature, important opportunities will emerge for developing more effective diagnostic and clinical management tools for these diseases.

A comparison of cataloged variation between International HapMap Consortium and 1000 Genomes Project data

BackgroundSince publication of the human genome in 2003, geneticists have been interested in risk variant associations to resolve the etiology of traits and complex diseases. The International HapMap Consortium undertook an effort to catalog all common variation across the genome (variants with a minor allele frequency (MAF) of at least 5% in one or more ethnic groups). HapMap along with advances in genotyping technology led to genome-wide association studies which have identified common variants associated with many traits and diseases. In 2008 the 1000 Genomes Project aimed to sequence 2500 individuals and identify rare variants and 99% of variants with a MAF of <1%.MethodsTo determine whether the 1000 Genomes Project includes all the variants in HapMap, we examined the overlap between single nucleotide polymorphisms (SNPs) genotyped in the two resources using merged phase II/III HapMap data and low coverage pilot data from 1000 Genomes.ResultsComparison of the two data sets showed that approximately 72% of HapMap SNPs were also found in 1000 Genomes Project pilot data. After filtering out HapMap variants with a MAF of <5% (separately for each population), 99% of HapMap SNPs were found in 1000 Genomes data.ConclusionsNot all variants cataloged in HapMap are also cataloged in 1000 Genomes. This could affect decisions about which resource to use for SNP queries, rare variant validation, or imputation. Both the HapMap and 1000 Genomes Project databases are useful resources for human genetics, but it is important to understand the assumptions made and filtering strategies employed by these projects.

The empirical power of rare variant association methods: results from sanger sequencing in 1,998 individuals.

The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.

Social network analysis and agent-based modeling in social epidemiology

The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed.

Data mining of high density genomic variant data for prediction of Alzheimer's disease risk

BackgroundThe discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.MethodsTwo different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.ResultsThe first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with APOE and GAB2 SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included APOE and GAB2 SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.ConclusionsWith the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.

3-Substituted coumarins as dual inhibitors of AChE and MAO for the treatment of Alzheimer's disease

The complex etiology of Alzheimer's disease (AD) has encouraged active research in the development of multi-target drugs with two or more complementary biological activities, since they may represent an important advance in the treatment of this disease. A series of 3-substituted coumarins were synthesized and evaluated as monoamino oxidases (MAO) and acetylcholinesterase (AChE) inhibitors. Most of the 3-benzamide coumarin derivatives inhibited both MAO-B and AChE with values in the micromolar range. It might be a promising direction for developing novel drugs as potential agents for the treatment of AD patients.

Genotype-Based Bayesian Analysis of Gene-Environment Interactions with Multiple Genetic Markers and Misclassification in Environmental Factors.

A key component to understanding etiology of complex diseases, such as cancer, diabetes, alcohol dependence, is to investigate gene-environment interactions. This work is motivated by the following two concerns in the analysis of gene-environment interactions. First, multiple genetic markers in moderate linkage disequilibrium may be involved in susceptibility to a complex disease. Second, environmental factors may be subject to misclassification. We develop a genotype based Bayesian pseudolikelihood approach that accommodates linkage disequilibrium in genetic markers and misclassification in environmental factors. Since our approach is genotype based, it allows the observed genetic information to enter the model directly thus eliminating the need to infer haplotype phase and simplifying computations. Bayesian approach allows shrinking parameter estimates towards prior distribution to improve estimation and inference when environmental factors are subject to misclassification. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a case-control study of interaction between early onset of drinking and genes involved in dopamine pathway.

Genetic variations in KIFC1 and the risk of aspirin exacerbated respiratory disease in a Korean population: an association analysis

Modest effects of genes in various pathways are significant in the etiology of complex human diseases, including aspirin exacerbated respiratory disease (AERD). By functioning as a relevant component of respiratory processes, the human kinesin family member C1 (KIFC1) is hypothesized to play a role in AERD pathogenesis. A case-control analysis was carried out by comparing the genotype distribution of six KIFC1 single-nucleotide polymorphisms between 93 AERD cases and 96 aspirin-tolerant asthma controls in a Korean population. After controlling for confounds, logistic and regression models via various modes of genetic inheritance facilitated the association analysis. Initial results revealed significant association at 0.05 level of significance between several KIFC1 variations and AERD (P=0.01-0.05, OR=1.81-1.90) as well as fall rate of forced expiratory volume in the 1st second, an important diagnostic marker of airways constriction (P=0.04-0.05). However, the signals were not deemed significant after multiple testing corrections (P (corr)>0.05). Although the results do not support a major role of KIFC1 in AERD pathogenesis in a Korean asthma cohort, further replication and validation studies are required to clarify the current findings.

Genetic control of gene expression in whole blood and lymphoblastoid cell lines is largely independent

The degree to which the level of genetic variation for gene expression is shared across multiple tissues has important implications for research investigating the role of expression on the etiology of complex human traits and diseases. In the last few years, several studies have been published reporting the extent of overlap in expression quantitative trait loci (eQTL) identified in multiple tissues or cell types. Although these studies provide important information on the regulatory control of genes across tissues, their limited power means that they can typically only explain a small proportion of genetic variation for gene expression. Here, using expression data from monozygotic twins (MZ), we investigate the genetic control of gene expression in lymphoblastoid cell lines (LCL) and whole blood (WB). We estimate the genetic correlation that represents the combined effects of all causal loci across the whole genome and is a measure of the level of common genetic control of gene expression between the two RNA sources. Our results show that, when averaged across the genome, mean levels of genetic correlation for gene expression in LCL and WB samples are close to zero. We support our results with evidence from gene expression in an independent sample of LCL, T-cells, and fibroblasts. In addition, we provide evidence that housekeeping genes, which maintain basic cellular functions, are more likely to have high genetic correlations between the RNA sources than non-housekeeping genes, implying a relationship between the transcript function and the degree to which a gene has tissue-specific genetic regulatory control.

METU-SNP: an integrated software system for SNP-complex disease association analysis.

Recently, there has been increasing research to discover genomic biomarkers, haplotypes, and potentially other variables that together contribute to the development of diseases. Single Nucleotide Polymorphisms (SNPs) are the most common form of genomic variations and they can represent an individual&rsquo;s genetic variability in greatest detail. Genome-wide association studies (GWAS) of SNPs, high-dimensional case-control studies, are among the most promising approaches for identifying disease causing variants. METU-SNP software is a Java based integrated desktop application specifically designed for the prioritization of SNP biomarkers and the discovery of genes and pathways related to diseases via analysis of the GWAS case-control data. Outputs of METU-SNP can easily be utilized for the downstream biomarkers research to allow the prediction and the diagnosis of diseases and other personalized medical approaches. Here, we introduce and describe the system functionality and architecture of the METU-SNP. We believe that the METU-SNP will help researchers with the reliable identification of SNPs that are involved in the etiology of complex diseases, ultimately supporting the development of personalized medicine approaches and targeted drug discoveries.

Polymorphisms of miRNAs genes are associated with the risk and prognosis of coronary artery disease

MicroRNAs (miRNAs) are small, single-stranded, non-protein-coding RNAs of about 22 nucleotides. miRNA molecules have been identified that plays key roles in a broad range of physiologic and pathologic processes. Polymorphisms in the corresponding sequence space are likely to make a significant contribution to phenotypic variation. To assess the variations of rs11614913 T→C in hsa-mir-196a2 and rs3746444 A→G in hsa-mir-499 in the complex etiology of coronary artery disease (CAD), 956 CAD patients diagnosed by coronary arterial angiography and 620 controls were enrolled. Among the patients, 785 (785/956) had complete follow-ups for 42months. The variant genotypes CC/CT of hsa-mir-196a2 rs11614913 T→C were not associated with a significantly increased risk of CAD (adjusted OR=1.02, 95% CI=0.76-1.38), compared with wide genotype TT, but CC and CC/CT genotypes were associated with 34 and 35% increased risks of serious prognosis of CAD (adjusted HR=1.34, 95% CI=1.02-1.75 for CC; adjusted HR=1.35, 95% CI=1.03-1.75 for CC/CT). In the variant of hsa-mir-499 rs3746444A→G, GG was associated with the 223% increased risk of CAD (adjusted OR=3.23, 95% CI=1.56-6.67). Cox regression analysis showed that age, smoking status, numbers of pathological changes in coronary arteries, rs11614913 T→C, and diabetes mellitus were associated with serious prognosis of CAD. Our findings strongly implicate the functional miRNAs polymorphisms of hsa-mir-196a2 and hsa-mir-499 genes may modulate the occurrence or prognosis in Chinese CAD.

A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.

Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach

Both common variants and rare variants are involved in the etiology of most complex diseases in humans. Developments in sequencing technology have led to the identification of a high density of rare variant single-nucleotide polymorphisms (SNPs) on the genome, each of which affects only at most 1% of the population. Genotypes derived from these SNPs allow one to study the involvement of rare variants in common human disorders. Here, we propose an association screening approach that treats genes as units of analysis. SNPs within a gene are used to create partitions of individuals, and inverse-probability weighting is used to overweight genotypic differences observed on rare variants. Association between a phenotype trait and the constructed partition is then evaluated. We consider three association tests (one-way ANOVA, chi-square test, and the partition retention method) and compare these strategies using the simulated data from the Genetic Analysis Workshop 17. Several genes that contain causal SNPs were identified by the proposed method as top genes.

Selection of representative SNP sets for genome-wide association studies: a metaheuristic approach

After the completion of Human Genome Project in 2003, it is now possible to associate genetic variations in the human genome with common and complex diseases. The current challenge now is to utilize the genomic data efficiently and to develop tools to improve our understanding of etiology of complex diseases. Many of the algorithms needed to deal with this task were originally developed in management science and operations research (OR). One application is to select a subset of the Single Nucleotide Polymorphism (SNP) biomarkers from the whole SNP set that is informative and small enough for subsequent association studies. In this paper, we present an OR application for representative SNP selection that implements our novel Simulated Annealing (SA) based feature-selection algorithm. We hope that our work will facilitate reliable identification of SNPs that are involved in the etiology of complex diseases and ultimately support timely identification of genomic disease biomarkers and the development of personalized-medicine approaches and targeted drug discoveries.

Do genome-wide association scans have potential for translation?

The success of genome-wide association studies (GWAS) in identifying replicating associations has greatly contributed to understanding of the genetic aetiology of complex diseases. This review discusses and provides examples of the potential of GWAS findings to be translated into clinical practice, i.e., diagnosis, prediction, prognosis, novel treatments and response to treatment of common diseases. The biological insights afforded by newly-identified robust associations represent the largest, albeit indirect, translational contribution of GWAS.