Association Study Era Research Articles

Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials.

Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).

Data analysis in the post-genome-wide association study era

Since the first report of a genome-wide association study (GWAS) on human age-related macular degeneration, GWAS has successfully been used to discover genetic variants for a variety of complex human diseases and/or traits, and thousands of associated loci have been identified. However, the underlying mechanisms for these loci remain largely unknown. To make these GWAS findings more useful, it is necessary to perform in-depth data mining. The data analysis in the post-GWAS era will include the following aspects: fine-mapping of susceptibility regions to identify susceptibility genes for elucidating the biological mechanism of action; joint analysis of susceptibility genes in different diseases; integration of GWAS, transcriptome, and epigenetic data to analyze expression and methylation quantitative trait loci at the whole-genome level, and find single-nucleotide polymorphisms that influence gene expression and DNA methylation; genome-wide association analysis of disease-related DNA copy number variations. Applying these strategies and methods will serve to strengthen GWAS data to enhance the utility and significance of GWAS in improving understanding of the genetics of complex diseases or traits and translate these findings for clinical applications.

Migraine genetics: New opportunities, new challenges

As migraine runs in families and has a strong genetic component, many investigations have aimed to identify the genetic factors that confer susceptibility to this disease. Initial studies of rare monogenic migraine disorders led to the identification of the first migraine genes (1). Specific mutations in the genes CACNA1A, ATP1A2 and SCN1A, which all encode subunits of ion transporters that play a part in neurotransmission, have been identified for familial hemiplegic migraine, an autosomal dominant subtype of migraine with aura characterized by a transient hemiparesis during the aura. Genes with an apparent vascular function were identified by studying monogenic diseases in which migraine is prominent, such as NOTCH3 in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy and CSNK1D in familial advanced sleep phase syndrome. As a result of a recent technical revolution in the way DNA analyses are performed, which allows costeffective massive genotyping in large cohorts of many thousands of patients, it is now feasible to identify causal genetic variants for common polygenic disorders. This technical breakthrough has led to what is often referred to as the genome-wide association study (GWAS) era. As a consequence, in the last decade, numerous GWASs have been performed, which have led to the discovery of thousands of genes and gene variants for hundreds of traits, including various common neurological diseases (2). An essential factor for a GWAS to be successful in migraine was the fact that clinicians and geneticists with an interest in migraine organized themselves in the International Headache Genetics Consortium (IHGC) (http:// www.headachegenetics.org/). The IHGC connects no less than 34 research groups from 12 countries from Europe, the USA and Australia and brings together an ever-increasing wealth of well-characterized genetic information and high-quality diagnostic data, as well as analysis expertise, to discover the basis for future treatments of migraine and other headache disorders. The success of IHGC’s gene discovery efforts is evident from four GWASs that investigated over 23,000 patients and hundreds of thousands of control subjects. Migraine-associated single nucleotide polymorphisms (SNPs) were identified in 13 loci and the assigned genes suggest the involvement of vascular (C7orf10, PHACTR1, TGFBR2) and neuronal (ASTN2, FHL5, LRP1, MEF2D, MMP16, MTDH, PHACTR1, PRDM16) pathways, as well as metalloproteinases (AJAP1, MMP16, TSPAN2) and a pain nociceptor (TRPM8) in migraine pathophysiology (3–6). As the identified gene variants, without exception, have small effect sizes (odds ratio< 1.2), a legitimate question is whether they will really further our understanding of migraine pathophysiology and have practical clinical applications. This special issue of Cephalalgia contains reviews that present recent findings and viewpoints on how genetic data can be used in research and the clinic. In addition, it contains several studies with original data, most of which were initiated by the IHGC, which address issues related to, for example, the genetic architecture of migraine subtypes, gene variants in relation to drug response and disease pathophysiology. The review by Chasman et al. (7) discusses the importance of the appropriate ascertainment of the migraine status of individual patients enrolled in genetic studies. Challenges and solutions in diagnosing patients in very large population-based cohorts and how this is different from clinic-based cohorts, where clinical information is, in general, more detailed, are extensively discussed. Because an increase in sample

SNPTracker: A Swift Tool for Comprehensive Tracking and Unifying dbSNP rs IDs and Genomic Coordinates of Massive Sequence Variants.

The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.

New Genetic and Genomic Approaches After the Genome-wide Association Study Era—Back to the Future

Identification of all human genes and their regulatory regions provides the essential framework for our understanding of the molecular basis of disease. There is a lot of enthusiasm for applying next-generation sequencing methods toward achieving the goals of precision medicine. To do so will require us to go beyond genomics and fundamentally understand how genomics impacts on biology and clinical outcomes through gene-gene and gene-environmental interactions. Clinicians and healthcare systems alike need to embrace the cultural and mindset change needed for the implementation of genomics in the clinic.

Genetics of adolescent idiopathic scoliosis in the post-genome-wide association study era.

Recently, analyses of a genome-wide association study (GWAS) conducted in a Japanese population found several common susceptibility variants associated with adolescent idiopathic scoliosis (AIS) (1-3). The top variants identified from this GWAS reside in genomic regions with potentially etiologically relevant genes and have been replicated across multiple populations and ethnicities. As a result, genes involved in biological pathways that are now viewed as promising targets for downstream functional investigation include abnormal somatosensory function ( LBX1 ) (1), delayed ossification of the developing spine ( GPR126 ) (2), skeletal dysplasia ( SOX9 ) (3) and scoliosis associated with syndromic disease ( KCNJ2 ) (3), One of these variants, a single nucleotide polymorphism (SNP) in the 3’-flanking region of the LBX1 gene (rs11190878) (1), has been consistently replicated in several independent populations (4). Other GWASes have also identified novel AIS-associated variants, with some overlap among the top ranked SNPs, including rs11190878 (Albertsen et al ., 2014, ASHG conference abstract) (5).

Gene-environment interactions in the study of asthma in the postgenomewide association studies era.

Asthma is a complex disease characterized by an intricate interplay of both heritable and environmental factors. Understanding the mechanisms through which genes and environment interact represents one of the major challenges for pulmonary researchers. This review provides an overview of the recently published literature on gene-environment (G × E) interactions in asthma, with a special focus on the new methodological developments in the postgenomewide association studies (GWAS) era. Most recent studies on G × E interaction in asthma used a candidate-gene approach. Candidate-gene studies considering exposure to outdoor air pollutants showed significant interactions mainly with variants in the GSTP1 gene on asthma in children. G × E studies on passive and active smoking, including one genomewide interaction study, identified novel genes of susceptibility to asthma and a time-dependent effect of maternal smoking. Other recent studies on asthma found interactions between candidate genes and occupational allergen exposure and several domestic exposures such as endotoxin and gas cooking. New methods were developed to efficiently estimate G × E interaction in GWAS, and a pathway-based strategy to select an enriched gene-set for G × E studies has recently been proposed. The G × E studies presented in this review offer a good example on how candidate-gene approaches can complement and help in validating GWAS findings.

The Anorexia Nervosa: Genetics Initiative (ANGI): Australia and New Zealand join forces

Anorexia nervosa (AN) is familial and heritable and the next logical step is to identify genes that influence risk. In the genome-wide association study (GWAS) era, for the study of complex traits such as eating disorders, large sample sizes are essential and necessitate global cooperation. The Anorexia Nervosa Genetics Initiative (ANGI) is a collaboration of researchers and clinicians from the United States, Sweden, Demark, and Australia and New Zealand who are collecting clinical information and DNA from 13,000 individuals with AN and controls. ANGI will unite with other global efforts to a goal of 25,000 participants with AN.

Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.

Studying complex diseases in the post genome-wide association studies (GWAS) era has led to developing methods that consider factor-sets rather than individual genetic/environmental factors (i.e., Multi-G-Multi-E studies), and mining for potential gene-environment (G×E) interactions has proven to be an invaluable aid in both discovery and deciphering underlying biological mechanisms. Current approaches for examining effect profiles in Multi-G-Multi-E analyses are either underpowered due to large degrees of freedom, ill-suited for detecting G×E interactions due to imprecise modeling of the G and E effects, or lack of capacity for modeling interactions between two factor-sets (e.g., existing methods focus primarily on a single E factor). In this work, we illustrate the issues encountered in constructing kernels for investigating interactions between two factor-sets, and propose a simple yet intuitive solution to construct the G×E kernel that retains the ease-of-interpretation of classic regression. We also construct a series of kernel machine (KM) score tests to evaluate the complete effect profile (i.e., the G, E, and G×E effects individually or in combination). We show, via simulations and a data application, that the proposed KM methods outperform the classic and PC regressions across a range of scenarios, including varying effect size, effect structure, and interaction complexity. The largest power gain was observed when the underlying effect structure involved complex G×E interactions; however, the proposed methods have consistent, powerful performance when the effect profile is simple or complex, suggesting that the proposed method could be a useful tool for exploratory or confirmatory G×E analysis.

Enlight: web-based integration of GWAS results with biological annotations.

Identifying causal variants remains a key challenge in post-GWAS (genome-wide association study) era, as many GWAS single-nucleotide polymorphisms (SNPs) (including imputed ones) fall into non-coding regions, making it difficult to associate statistical significance with predicted functionality. Therefore, we created a web-based tool, Enlight, which overlays functional annotation information, such as histone modification states, methylation patterns, transcription factor binding sites, eQTL and higher-order chromosomal structure, to GWAS results. Accessible by a Web browser at http://enlight.usc.edu.

Systematic assessment of imputation performance using the 1000 Genomes reference panels.

Genotype imputation has been widely adopted in the postgenome-wide association studies (GWAS) era. Owing to its ability to accurately predict the genotypes of untyped variants, imputation greatly boosts variant density, allowing fine-mapping studies of GWAS loci and large-scale meta-analysis across different genotyping arrays. By leveraging genotype data from 90 whole-genome deeply sequenced individuals as the evaluation benchmark and the 1000 Genomes Project data as reference panels, we systematically examined four important issues related to genotype imputation practice. First, in a study of imputation accuracy, we found that IMPUTE2 and minimac have the best imputation performance among the three popular imputing software evaluated and that using a multi-population reference panel is beneficial. Second, the optimal imputation quality cutoff for removing poorly imputed variants varies according to the software used. Third, the major contributing factors to consistently poor imputation are low variant heterozygosity, high sequence similarity to other genomic regions, high GC content, segmental duplication and being far from genotyping markers. Lastly, in an evaluation of the imputability of all known GWAS regions, we found that GWAS loci associated with hematological measurements and immune system diseases are harder to impute, as compared with other human traits. Recommendations made based on the above findings may provide practical guidance for imputation exercise in future genetic studies.

IL10 low‐frequency variants in Behçet's disease patients

To explain the missing heritability after the genome-wide association studies era, sequencing studies allow the identification of low-frequency variants with a stronger effect on disease risk. Common variants in the interleukin 10 gene (IL10) have been consistently associated with Behçet's disease (BD) and the goal of this study is to investigate the role of low-frequency IL10 variants in BD susceptibility. To identify IL10 low-frequency variants, a discovery group of 50 Portuguese BD patients were Sanger-sequenced in a 7.7kb genomic region encompassing the complete IL10 gene, 0.9kb upstream and 2kb downstream, and two conserved regions in the putative promoter. To assess if the novel variants are BD- and/or Portuguese-specific, they were assayed in an additional group of BD patients (26 Portuguese and 964 Iranian) and controls (104 Portuguese and 823 Iranian). Rare IL10 coding variants were not detected in BD patients, but we identified 28 known single nucleotide polymorphisms with minor allele frequencies ranging from 0.010 to 0.390, and five novel non-coding variants in five heterozygous cases. ss836185595, located in the IL10 3' untranslated region, was also detected in one Iranian control individual and therefore is not specific to BD. The remaining novel IL10 variants (ss836185596 and ss836185602 in intron 3, ss836185598 and ss836185604 in the putative promoter region) were not found in the replication dataset. This study highlights the importance of screening the whole gene and regulatory regions when searching for novel variants associated with complex diseases, and the need to develop bioinformatics tools to predict the functional impact of non-coding variants and statistical tests which incorporate these predictions.

Genetic variant of the renin-angiotensin system and prevalence of type 2 diabetes mellitus: a modest but significant effect of aldosterone synthase

Recent genome-wide association studies have identified multiple variants that confer risk of type 2 diabetes mellitus (DM). However, established associations explain only a part of the heritability. Thus, even at the genome-wide association studies era, candidate gene approach should be still useful. Recent interventional studies against the renin-angiotensin system (RAS) showed reduction in new onset of DM, implying the system is involved in the onset. We substantiated the hypothesis that genetic variants of RAS have significant association with prevalence of DM. We enrolled to the study consecutive 782 subjects who had consulted our hospitals for mainly lifestyle related diseases. They consisted of 282 (36.1 %) diabetes cases. Genotypes were assayed with genomic DNA for conventional four genes of the RAS, i.e., angiotensin converting enzyme (ACE) insertion/deletion variant, angiotensinogen (AGT) M235T variant, angiotensin II type I receptor (AT1) A1166C variant, and aldosterone synthase (CYP11B2) C-344T variant. Association between the genetic variants of the RAS and prevalence of type 2 DM was tested. A significant association of DM and CYP11B2 genotype was obtained. There was no significant association between DM and ACE, AGT and AT1 variants. A multivariate logistic regression showed that age, gender, and CYP11B2 genotype were independent factors for association to diabetes, the DM risk of CC/CT to TT being 1.40 (95 % CI 1.04–1.90, p = 0.029). Thus, it is concluded that a genetic variant of the RAS should have a modest but significant impact on the onset of type 2 diabetes mellitus.

Deciphering the complex: Methodological overview of statistical models to derive OMICS‐based biomarkers

Recent technological advances in molecular biology have given rise to numerous large-scale datasets whose analysis imposes serious methodological challenges mainly relating to the size and complex structure of the data. Considerable experience in analyzing such data has been gained over the past decade, mainly in genetics, from the Genome-Wide Association Study era, and more recently in transcriptomics and metabolomics. Building upon the corresponding literature, we provide here a nontechnical overview of well-established methods used to analyze OMICS data within three main types of regression-based approaches: univariate models including multiple testing correction strategies, dimension reduction techniques, and variable selection models. Our methodological description focuses on methods for which ready-to-use implementations are available. We describe the main underlying assumptions, the main features, and advantages and limitations of each of the models. This descriptive summary constitutes a useful tool for driving methodological choices while analyzing OMICS data, especially in environmental epidemiology, where the emergence of the exposome concept clearly calls for unified methods to analyze marginally and jointly complex exposure and OMICS datasets.

Empirical Hierarchical Bayes Approach to Gene‐Environment Interactions: Development and Application to Genome‐Wide Association Studies of Lung Cancer in TRICL

The analysis of gene-environment (G × E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871-882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G × E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219-226) identifies markers with low G × E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach.

The 1000 Genomes Project: paving the way for personalized genomic medicine.

The 1000 Genomes Project: paving the way for personalized genomic medicine.

Advances in Stroke

The field of stroke genetics continues to make substantive advances. Large-scale partnerships have led to meta-analyses in ischemic and hemorrhagic stroke, which are yielding reproducible genetic risk factors. Most ischemic stroke risk factors seem to be specific to type, for example, large-vessel stroke. Progress is being made in understanding the pathophysiology of single-gene stroke syndromes. Genetics is also being used to potentially advance pharmacotherapeutics. In the genome-wide association study (GWAS) era, several themes are emerging. First, heritability seems to vary by ischemic stroke subtype. Using complex trait analysis on a GWAS data set of >3000 individuals, Bevan et al1 found a 37.9% heritability for ischemic stroke overall, but the heritability varied widely by subtype from a high of 40.3% for large-vessel stroke to a low of 16.1% for small-vessel stroke.1 Second, genetic risk factors seem to be subtype-selective. Third, GWAS have yielded more reliable discoveries than candidate gene studies uninformed by GWAS. In the previously cited study by Bevan,1 no candidate gene previously reported as associated with ischemic stroke could be replicated using GWAS data, but 3 loci from related cardiovascular GWAS were significant even after rigorous correction for multiple comparisons. In the area of ischemic stroke genetics, a partnership between the International Stroke Genetics Consortium and the Wellcome Trust Case Control Consortium 2, which included GWAS data from 5859 cases and 6281 controls, replicated associations …

The curse of the missing heritability

The curse of the missing heritability

Pharmacogenomics discovery and implementation in genome‐wide association studies era

Clinical response to therapeutic treatments often varies among individual patients, ranging from beneficial effect to even fatal adverse reaction. Pharmacogenomics holds the promise of personalized medicine through elucidating genetic determinants responsible for pharmacological outcomes (e.g., cytotoxicities to anticancer drugs) and therefore guide the prescription decision prior to drug treatment. Besides traditional candidate gene-based approaches, technical advances have begun to allow application of whole-genome approaches to pharmacogenomic discovery. In particular, comprehensive understanding of human genetic variation provides the basis for applying GWAS (genome-wide association studies) in pharmacogenomic research to identify genomic loci associated with pharmacological phenotypes (e.g., individual dose requirement for warfarin). We therefore briefly reviewed the background for pharmacogenetic/pharmacogenomic research with statins and warfarin as examples for the GWAS discovery and their clinical implementation. In conclusion, with some challenges, whole-genome approaches such as GWAS have allowed unprecedented progress in identifying genetic variants associated with pharmacological phenotypes, as well as provided foundation for the next wave of pharmacogenomic discovery utilizing sequencing-based approaches. Furthermore, investigation of the complex interactions among genetic and epigenetic factors on the whole-genome scale will become the post-GWAS research focus for pharmacologic complex traits.

The quest for psoriasis susceptibility genes in the postgenome-wide association studies era: charting the road ahead

The International Psoriasis Council, a global nonprofit organization dedicated to advancing psoriasis research and treatment, organized its inaugural genetics workshop in Montreal, on 12 October 2011. The presentations included a summary of the remarkable progress achieved through the implementation of genome-wide association studies, which have highlighted key pathogenic pathways for psoriasis. Ongoing meta-analyses are identifying further susceptibility genes, bringing the number of known loci close to 40. The functional characterization of low-risk alleles is proving problematic, but next-generation sequencing approaches are expected to identify rare deleterious variants, which will be easier to investigate. Elucidating the genetic architecture of psoriasis will have major implications in terms of understanding disease mechanisms and predicting response to treatment.