Future Genome-wide Association Studies Research Articles

Post-GWAS: where next? More samples, more SNPs or more biology?

The power of genome-wide association studies (GWAS) rests on several foundations: (i) there is a significant amount of additive genetic variation, (ii) individual causal polymorphisms often have sizable effects and (iii) they segregate at moderate-to-intermediate frequencies, or will be effectively 'tagged' by polymorphisms that do. Each of these assumptions has recently been questioned. (i) Why should genetic variation appear additive given that the underlying molecular networks are highly nonlinear? (ii) A new generation of relatedness-based analyses directs us back to the nearly infinitesimal model for effect sizes that quantitative genetics was long based upon. (iii) Larger effect causal polymorphisms are often low frequency, as selection might lead us to expect. Here, we review these issues and other findings that appear to question many of the foundations of the optimism GWAS prompted. We then present a roadmap emerging as one possible future for quantitative genetics. We argue that in future GWAS should move beyond purely statistical grounds. One promising approach is to build upon the combination of population genetic models and molecular biological knowledge. This combined treatment, however, requires fitting experimental data to models that are very complex, as well as accurate capturing of the uncertainty of resulting inference. This problem can be resolved through Bayesian analysis and tools such as approximate Bayesian computation-a method growing in popularity in population genetic analysis. We show a case example of anterior-posterior segmentation in Drosophila, and argue that similar approaches will be helpful as a GWAS augmentation, in human and agricultural research.

Identification of Functionalcis-regulatory Polymorphisms in the Human Genome

Polymorphisms in regulatory DNA regions are believed to play an important role in determining phenotype, including disease, and in providing raw material for evolution. We devised a new pipeline for the systematic identification of functional variation in human regulatory sequences. The algorithm is based on the identification of SNPs leading to significant changes in both the affinity of a regulatory region for transcription factors (TFs) and the expression in vivo of the regulated gene. We tested the algorithm by identifying SNPs leading to altered regulation by STAT3 in human promoters and introns, and experimentally validated the top-scoring ones, showing that most of the SNPs identified by the algorithm indeed correspond to differential binding of STAT3 and differential induction of the target gene upon stimulation with IL6. Using the same computational approach, we compiled a database of thousands of predicted functional regulatory SNPs for hundreds of human TFs, which we provide as online Supporting Information. We discuss possible applications to the interpretation of noncoding SNPs associated with human diseases. The method we propose and the database of predicted functional cis-regulatory polymorphisms will be useful in future studies of regulatory variation and in particular to interpret the results of past and future genome-wide association studies.

The impact of improved microarray coverage and larger sample sizes on future genome-wide association studies.

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with complex traits. However, the genetic heritability of most of these traits remains unexplained. To help guide future studies, we address the crucial question of whether future GWAS can detect new SNP associations and explain additional heritability given the new availability of larger GWAS SNP arrays, imputation, and reduced genotyping costs. We first describe the pairwise and imputation coverage of all SNPs in the human genome by commercially available GWAS SNP arrays, using the 1000 Genomes Project as a reference. Next, we describe the findings from 6 years of GWAS of 172 chronic diseases, calculating the power to detect each of them while taking array coverage and sample size into account. We then calculate the power to detect these SNP associations under different conditions using improved coverage and/or sample sizes. Finally, we estimate the percentages of SNP associations and heritability previously detected and detectable by future GWAS under each condition. Overall, we estimated that previous GWAS have detected less than one-fifth of all GWAS-detectable SNPs underlying chronic disease. Furthermore, increasing sample size has a much larger impact than increasing coverage on the potential of future GWAS to detect additional SNP-disease associations and heritability.

Genetic diversity in the modern horse illustrated from genome-wide SNP data.

Horses were domesticated from the Eurasian steppes 5,000–6,000 years ago. Since then, the use of horses for transportation, warfare, and agriculture, as well as selection for desired traits and fitness, has resulted in diverse populations distributed across the world, many of which have become or are in the process of becoming formally organized into closed, breeding populations (breeds). This report describes the use of a genome-wide set of autosomal SNPs and 814 horses from 36 breeds to provide the first detailed description of equine breed diversity. FST calculations, parsimony, and distance analysis demonstrated relationships among the breeds that largely reflect geographic origins and known breed histories. Low levels of population divergence were observed between breeds that are relatively early on in the process of breed development, and between those with high levels of within-breed diversity, whether due to large population size, ongoing outcrossing, or large within-breed phenotypic diversity. Populations with low within-breed diversity included those which have experienced population bottlenecks, have been under intense selective pressure, or are closed populations with long breed histories. These results provide new insights into the relationships among and the diversity within breeds of horses. In addition these results will facilitate future genome-wide association studies and investigations into genomic targets of selection.

Considering trauma exposure in the context of genetics studies of posttraumatic stress disorder: a systematic review.

BackgroundPosttraumatic stress disorder (PTSD) is a debilitating anxiety disorder. Surveys of the general population suggest that while 50-85% of Americans will experience a traumatic event in their lifetime, only 2-50% will develop PTSD. Why some individuals develop PTSD following trauma exposure while others remain resilient is a central question in the field of trauma research. For more than half a century, the role of genetic influences on PTSD has been considered as a potential vulnerability factor. However, despite the exponential growth of molecular genetic studies over the past decade, limited progress has been made in identifying true genetic variants for PTSD.MethodsIn an attempt to aid future genome wide association studies (GWAS), this paper presents a systematic review of 28 genetic association studies of PTSD. Inclusion criteria required that 1) all participants were exposed to Criterion A traumatic events, 2) polymorphisms of relevant genes were genotyped and assessed in relation to participants’ PTSD status, 3) quantitative methods were used, and 4) articles were published in English and in peer-reviewed journals. In the examination of these 28 studies, particular attention was given to variables related to trauma exposure (e.g. number of traumas, type of trauma).ResultsResults indicated that most articles did not report on the GxE interaction in the context of PTSD or present data on the main effects of E despite having data available. Furthermore, some studies that did consider the GxE interaction had significant findings, underscoring the importance of examining how genotypes can modify the effect of trauma on PTSD. Additionally, results indicated that only a small number of genes continue to be studied and that there were marked differences in methodologies across studies, which subsequently limited robust conclusions.ConclusionsAs trauma exposure is a necessary condition for the PTSD diagnosis, this paper identifies gaps in the current literature as well as provides recommendations for how future GWAS studies can most effectively incorporate trauma exposure data in both the design and analysis phases of studies.

CA4-05: Electronic Health Record Phenotyping to Define Rate of Extreme Weight Gain Associated with the Use of 2nd/3rd Generation Antipsychotic Medications

Background/AimsWeight gain is an undesirable side effect of treatment with 2nd/3rd generation antipsychotic drugs which may have genetic determinants. A number of candidate genes have been analyzed but no genome-wide association studies (GWAS) have yet been reported. The purpose of this study was to determine the rate of extreme weight gain (EWG) among adult users of 2nd/3rd generation antipsychotic medications for future GWAS.MethodsA standardized dataset was extracted on antipsychotic medications users at Group Health and Geisinger Health System from 2004–2010 that included demographics, enrollment, vitals, and pharmacy data. Electronic health record search algorithms were used to identify adult subjects with body weight increases of 15%+ within one year of the initiation of new 2nd/3rd generation antipsychotic drugs. To adjudicate the association of EWG with drug treatment, SAS was used to plot and visually inspect a single graph for each episode that encompassed all weight measurements, antipsychotic orders/fills, pregnancy- and cancer-related visits, and steroid and insulin orders/fills over time. The rate of EWG was then compared among the common types of antipsychotic medications.ResultsThere were 7128 episodes of antipsychotic use that qualified for analysis (5251 patients). The most common medication types were quetiapine (n=2543), risperidone (n=1817), aripiprazole (n=1315), olanzapine (n=971), and ziprasidone (n=413). The least common medications were clozapine (n=37), olanzapine/fluoxetine (n=23), and paliperidone (n=9). EWG was identified for 275 of the 7128 episodes (3.86 per 100 episodes). EWG was significantly different among common medication types (p-value<0.0001). EWG was highest for olanzapine (5.46 per 100 episodes, 95% CI=[4.77, 6.24]), followed by quetiapine (3.93 per 100 episodes, 95% CI=[3.25, 4.75]), risperidone (3.58 per 100 episodes, 95% CI=[3.14, 4.07]), and aripiprazole (3.50 per 100 episodes, 95% CI=[2.65, 4.62]). For ziprasidone, EWG was 2–3 fold lower (1.69 per 100 episodes, 95% CI=[1.46, 1.96]). EWG was not significantly different between Geisinger and Group Health (4.43 per 100 episodes versus 3.65 per 100 episodes, p=0.157).DiscussionRates of EWG differed significantly by type of 2nd/3rd generation antipsychotic medication, which was consistent in two geographically diverse populations. Future GWAS may help determine if genetic determinants of EWG are drug specific.

A review of family history of cardiovascular disease: risk factor and research tool

Family history (FH) studies have been used to quantify the heritable component of diseases for centuries. Genome-wide association studies (GWAS) in both coronary artery disease (CAD) and stroke have implicated several gene loci in these diseases and have shed light on biological mechanisms, but have not yet yielded fruit in terms of clinical application, partly because of the complexity of gene-gene and gene-environment interactions. Family history studies remain the most accessible way of measuring the inherited component of a disease and they represent the overall interaction between environmental and genetic factors. The current knowledge base for FH of stroke and CAD and disease correlates are evaluated. FH of stroke and CAD are inconsistently recorded in clinical practice, partly because of lack of data regarding family history of stroke and CAD in prospective population studies. Future FH studies are necessary to characterise the role of FH in prognosis and risk prediction of contemporary populations, but also to guide future studies of genetics and epigenetics. In this article, the study design and methodology of family history studies are reviewed. The Oxford Vascular Study (OXVASC) is an ongoing prospective, population-based study of CAD and stroke with very high levels of clinical ascertainment, which allows detailed study of FH, and has already shown important new findings. Such data may help to formulate improved risk prediction tools and to inform future GWAS.

Cardiovascular Pharmacogenomics: Current Status and Future Directions—Report of a National Heart, Lung, and Blood Institute Working Group

The National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group on January 7, 2011, at George Washington University in Washington, DC, to provide recommendations to the NHLBI that would guide informed decisions on research directions and priorities in the field of cardiovascular

Unraveling Genes, Hormones, and Breast Cancer

Unraveling Genes, Hormones, and Breast Cancer

Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the ovarian cancer association consortium (OCAC)

In this article, we review the current knowledge of the inherited genetics of epithelial ovarian cancer (EOC) susceptibility and clinical outcome. We focus on recent developments in identifying low-penetrance susceptibility genes and the role of the Ovarian Cancer Association Consortium (OCAC) in these discoveries. The OCAC was established to facilitate large-scale replication analyses for reported genetic associations for EOC. Since its inception, the OCAC has conducted both candidate gene and genome-wide association studies (GWAS); the latter has identified six established loci for EOC susceptibility, most of which showed stronger association with the serous histological subtype. Future GWAS and sequencing studies are likely to result in the discovery of additional susceptibility loci and may result in established associations with clinical outcome. Additional rare and uncommon ovarian cancer loci will likely be uncovered from high-throughput next-generation sequencing studies. Applying these novel findings to establish improved preventative and clinical intervention strategies will be one of the major challenges of future work.

Genetic mapping of recurrent exertional rhabdomyolysis in a population ofNorthAmericanThoroughbreds

Recurrent exertional rhabdomyolysis is a heritable disorder that results in painful skeletal muscle cramping with exercise in up to 10% of all Thoroughbred racehorses. Here, we report a genome-wide association study with 48 282 SNPs analyzed among 48 case and 37 control Thoroughbreds. The most significant SNPs spanned approximately 13 Mb on ECA16, and the P-value of the most significant SNP after correcting for population structure was 8.0 × 10(-6) . This region on ECA16 was further evaluated by genotyping 247 SNPs in both the initial population and a second population of 34 case and 98 control Thoroughbreds. Several SNPs across the 13-Mb region on ECA16 showed significance when each population was analyzed separately; however, the exact positions of the most significant SNPs within this region on ECA16 varied between populations. This variability in location may be attributed to lack of power owing to insufficient sample sizes within each population individually, or to the relative distribution of long, conserved haplotypes, characteristic of the Thoroughbred breed. Future genome-wide association studies with additional horses would likely improve the power to resolve casual loci located on ECA16 and increase the likelihood of detecting any additional loci on other chromosomes contributing to disease susceptibility.

Unrecognized sequence homologies may confound genome-wide association studies

Genome-wide association studies (GWAS) have become a preferred method to identify new genetic susceptibility loci. This technique aims to understanding the molecular etiology of common diseases, but in many cases, it has led to the identification of loci with no obvious biological relevance. Herein, we show that previously unrecognized sequence homologies have caused single-nucleotide polymorphism (SNP) microarrays to incorrectly associate a phenotype to a given locus when in fact the linkage is to another distant locus. Using genetic differences between male and female subjects as a model to study the effect of one specific genomic region on the whole SNP microarray, we provide strong evidence that the use of standard methods for GWAS can be misleading. We suggest a new systematic quality control step in the biological interpretation of previous and future GWAS.

Genome-wide association studies in lung disease

Genome-wide association studies (GWAS) have provided new insights into the molecular mechanisms of lung function and lung diseases. GWAS, and studies that build upon their findings, will continue to provide...

The Genetic Structure of the Swedish Population

Patterns of genetic diversity have previously been shown to mirror geography on a global scale and within continents and individual countries. Using genome-wide SNP data on 5174 Swedes with extensive geographical coverage, we analyzed the genetic structure of the Swedish population. We observed strong differences between the far northern counties and the remaining counties. The population of Dalarna county, in north middle Sweden, which borders southern Norway, also appears to differ markedly from other counties, possibly due to this county having more individuals with remote Finnish or Norwegian ancestry than other counties. An analysis of genetic differentiation (based on pairwise Fst) indicated that the population of Sweden's southernmost counties are genetically closer to the HapMap CEU samples of Northern European ancestry than to the populations of Sweden's northernmost counties. In a comparison of extended homozygous segments, we detected a clear divide between southern and northern Sweden with small differences between the southern counties and considerably more segments in northern Sweden. Both the increased degree of homozygosity in the north and the large genetic differences between the south and the north may have arisen due to a small population in the north and the vast geographical distances between towns and villages in the north, in contrast to the more densely settled southern parts of Sweden. Our findings have implications for future genome-wide association studies (GWAS) with respect to the matching of cases and controls and the need for within-county matching. We have shown that genetic differences within a single country may be substantial, even when viewed on a European scale. Thus, population stratification needs to be accounted for, even within a country like Sweden, which is often perceived to be relatively homogenous and a favourable resource for genetic mapping, otherwise inferences based on genetic data may lead to false conclusions.

Implication of next-generation sequencing on association studies

BackgroundNext-generation sequencing technologies can effectively detect the entire spectrum of genomic variation and provide a powerful tool for systematic exploration of the universe of common, low frequency and rare variants in the entire genome. However, the current paradigm for genome-wide association studies (GWAS) is to catalogue and genotype common variants (5% < MAF). The methods and study design for testing the association of low frequency (0.5% < MAF ≤ 5%) and rare variation (MAF ≤ 0.5%) have not been thoroughly investigated. The 1000 Genomes Project represents one such endeavour to characterize the human genetic variation pattern at the MAF = 1% level as a foundation for association studies. In this report, we explore different strategies and study designs for the near future GWAS in the post-era, based on both low coverage pilot data and exon pilot data in 1000 Genomes Project.ResultsWe investigated the linkage disequilibrium (LD) pattern among common and low frequency SNPs and its implication for association studies. We found that the LD between low frequency alleles and low frequency alleles, and low frequency alleles and common alleles are much weaker than the LD between common and common alleles. We examined various tagging designs with and without statistical imputation approaches and compare their power against de novo resequencing in mapping causal variants under various disease models. We used the low coverage pilot data which contain ~14 M SNPs as a hypothetical genotype-array platform (Pilot 14 M) to interrogate its impact on the selection of tag SNPs, mapping coverage and power of association tests. We found that even after imputation we still observed 45.4% of low frequency SNPs which were untaggable and only 67.7% of the low frequency variation was covered by the Pilot 14 M array.ConclusionsThis suggested GWAS based on SNP arrays would be ill-suited for association studies of low frequency variation.

Using linkage information to weight a genome‐wide association of bipolar disorder

Issues of multiple-testing and statistical significance in genomewide association studies (GWAS) have prompted statistical methods utilizing prior data to increase the power of association results. Using prior findings from genome-wide linkage studies on bipolar disorder (BPD), we employed a weighted false discovery approach (wFDR; [Roeder et al. 2006. Am J Hum Genet 78(2): 243–252]) to previously reported GWAS data drawn from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Using this method, association signals are up or down-weighted given the linkage score in that genomic region. Although no SNPs in our sample reached genome-wide significance through the wFDR approach, the strongest single SNP result from the original GWAS results (rs4939921 in myosin VB) is strongly up-weighted as it occurs on a linkage peak of chromosome 18. We also identify regions on chromosome 9, 17, and 18 where modestly associated SNP clusters coincide with strong linkage scores, implicating them as possible candidate regions for further analysis. Moving forward, we believe the application of prior linkage information will be increasingly useful to future GWAS studies that incorporate rarer variants into their analysis.

Genome-wide association study SNPs in the human genome diversity project populations: does selection affect unlinked SNPs with shared trait associations?

Genome-wide association studies (GWAS) have identified more than 2,000 trait-SNP associations, and the number continues to increase. GWAS have focused on traits with potential consequences for human fitness, including many immunological, metabolic, cardiovascular, and behavioral phenotypes. Given the polygenic nature of complex traits, selection may exert its influence on them by altering allele frequencies at many associated loci, a possibility which has yet to be explored empirically. Here we use 38 different measures of allele frequency variation and 8 iHS scores to characterize over 1,300 GWAS SNPs in 53 globally distributed human populations. We apply these same techniques to evaluate SNPs grouped by trait association. We find that groups of SNPs associated with pigmentation, blood pressure, infectious disease, and autoimmune disease traits exhibit unusual allele frequency patterns and elevated iHS scores in certain geographical locations. We also find that GWAS SNPs have generally elevated scores for measures of allele frequency variation and for iHS in Eurasia and East Asia. Overall, we believe that our results provide evidence for selection on several complex traits that has caused changes in allele frequencies and/or elevated iHS scores at a number of associated loci. Since GWAS SNPs collectively exhibit elevated allele frequency measures and iHS scores, selection on complex traits may be quite widespread. Our findings are most consistent with this selection being either positive or negative, although the relative contributions of the two are difficult to discern. Our results also suggest that trait-SNP associations identified in Eurasian samples may not be present in Africa, Oceania, and the Americas, possibly due to differences in linkage disequilibrium patterns. This observation suggests that non-Eurasian and non-East Asian sample populations should be included in future GWAS.

Unraveling the genetics of otitis media: from mouse to human and back again

Otitis media (OM) is among the most common illnesses of early childhood, characterised by the presence of inflammation in the middle ear cavity. Acute OM and chronic OM with effusion (COME) affect the majority of children by school age and have heritability estimates of 40-70%. However, the majority of genes underlying this susceptibility are, as yet, unidentified. One method of identifying genes and pathways that may contribute to OM susceptibility is to look at mouse mutants displaying a comparable phenotype. Single-gene mouse mutants with OM have identified a number of genes, namely, Eya4, Tlr4, p73, MyD88, Fas, E2f4, Plg, Fbxo11, and Evi1, as potential and biologically relevant candidates for human disease. Recent studies suggest that this "mouse-to-human" approach is likely to yield relevant data, with significant associations reported between polymorphisms at the FBXO11, TLR4, and PAI1 genes and disease in humans. An association between TP73 and chronic rhinosinusitis has also been reported. In addition, the biobanks of available mouse mutants provide a powerful resource for functional studies of loci identified by future genome-wide association studies of OM in humans. Mouse models of OM therefore are an important component of current approaches attempting to understand the complex genetic susceptibility to OM in humans, and which aim to facilitate the development of preventative and therapeutic interventions for this important and common disease.

Analysing biological pathways in genome-wide association studies

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

Population Structure of Aggarwals of North India as Revealed by Molecular Markers

Using molecular genetic data on Aggarwals (Vaish/Vysya), an endogamous population group of north India, we provide evidence of its homogeneous unstratified population structure. We found the mean average heterozygosity value of 0.33 for 14 single nucleotide polymorphisms belonging to four genes (TCF7L2-, HHEX-, KCNJ11-, and ADIPOQ-) in the Aggarwal population (sample of 184 individuals) and tried to evaluate the genomic efficiency of endogamy in this population with the help of clan-based stratified analysis. We concluded that the sociocultural identity of the endogamous population groups could act as a robust proxy maker for inferring their homogeneity and population structure in India, which is ideal also for population selection for future genome-wide association studies in the country.