Planning a genome-wide association study: points to consider

SNP characteristics predict replication success in association studies

Successful independent replication is the most direct approach for distinguishing real genotype-disease associations from false discoveries in genome-wide association studies (GWAS). Selecting SNPs for replication has been primarily based on P values from the discovery stage, although additional characteristics of SNPs may be used to improve replication success. We used disease-associated SNPs from more than 2,000 published GWASs to identify predictors of SNP reproducibility. SNP reproducibility was defined as a proportion of successful replications among all replication attempts. The study reporting association for the first time was considered to be discovery and all consequent studies targeting the same phenotype replications. We found that -Log(P), where P is a P value from the discovery study, is the strongest predictor of the SNP reproducibility. Other significant predictors include type of the SNP (e.g., missense vs intronic SNPs) and minor allele frequency. Features of the genes linked to the disease-associated SNP also predict SNP reproducibility. Based on empirically defined rules, we developed a reproducibility score (RS) to predict SNP reproducibility independently of -Log(P). We used data from two lung cancer GWAS studies as well as recently reported disease-associated SNPs to validate RS. Minus Log(P) outperforms RS when the very top SNPs are selected, while RS works better with relaxed selection criteria. In conclusion, we propose an empirical model to predict SNP reproducibility, which can be used to select SNPs for validation and prioritization.

Advancing avian behavioral neuroendocrinology through genomics

Genome technologies are transforming all areas of biology, including the study of hormones, brain and behavior. Annotated reference genome assemblies are rapidly being produced for many avian species. Here we briefly review the basic concepts and tools used in genomics. We then consider how these are informing the study of avian behavioral neuroendocrinology, focusing in particular on lessons from the study of songbirds. We discuss the impact of having a complete "parts list" for an organism; the transformational potential of studying large sets of genes at once instead one gene at a time; the growing recognition that environmental and behavioral signals trigger massive shifts in gene expression in the brain; and the prospects for using comparative genomics to uncover the genetic roots of behavioral variation. Throughout, we identify promising new directions for bolstering the application of genomic information to further advance the study of avian brain and behavior.

Has discovery-based cancer research been a bust?

The completion of the human genome sequence sparked optimism about prospects for new anticancer drug development, but clinical progress over the last decade has proven slower than expected. Here it is proposed that unrealistically high expectations of first-generation discovery-based diagnostics have contributed to this problem. Hypothesis-based single-molecule tests (e.g., mutation screening of KRAS, EGFR, BRAF or KIT genes) continue to change clinical practice incrementally, whereas first-generation multiplex assays--such as gene expression profiling and proteomics--have identified few high-impact therapeutic targets despite numerous correlations with prognosis. To move forward, second-generation multiplex diagnostics should be based not on statistical patterns/associations alone, but on clinically interpretable ('high-signal-to-noise') data such as change-of-function mutations, gene amplifications, recurrent chromosomal anomalies, and abnormal phosphorylation profiles of ERK or mTOR signaling cascades.

The genomic signature of trait-associated variants

BACKGROUND

Genome-wide association studies have identified thousands of SNP variants associated with hundreds of phenotypes. For most associations the causal variants and the molecular mechanisms underlying pathogenesis remain unknown. Exploration of the underlying functional annotations of trait-associated loci has thrown some light on their potential roles in pathogenesis. However, there are some shortcomings of the methods used to date, which may undermine efforts to prioritize variants for further analyses. Here, we introduce and apply novel methods to rigorously identify annotation classes showing enrichment or depletion of trait-associated variants taking into account the underlying associations due to co-location of different functional annotations and linkage disequilibrium.

RESULTS

We assessed enrichment and depletion of variants in publicly available annotation classes such as genic regions, regulatory features, measures of conservation, and patterns of histone modifications. We used logistic regression to build a multivariate model that identified the most influential functional annotations for trait-association status of genome-wide significant variants. SNPs associated with all of the enriched annotations were 8 times more likely to be trait-associated variants than SNPs annotated with none of them. Annotations associated with chromatin state together with prior knowledge of the existence of a local expression QTL (eQTL) were the most important factors in the final logistic regression model. Surprisingly, despite the widespread use of evolutionary conservation to prioritize variants for study we find only modest enrichment of trait-associated SNPs in conserved regions.

CONCLUSION

We established odds ratios of functional annotations that are more likely to contain significantly trait-associated SNPs, for the purpose of prioritizing GWAS hits for further studies. Additionally, we estimated the relative and combined influence of the different genomic annotations, which may facilitate future prioritization methods by adding substantial information.

Current and emerging technology approaches in genomics

PURPOSE

To introduce current and emerging approaches that are being utilized in the field of genomics so the reader can conceptually evaluate the literature and appreciate how these approaches are advancing our understanding of health-related issues.

ORGANIZING CONSTRUCT

Each approach is described and includes information related to how it is advancing research, its potential clinical utility, exemplars of current uses, challenges related to technologies used for these approaches, and when appropriate information related to understanding the evidence base for clinical utilization of each approach is provided. Web-based resources are included for the reader who would like more in-depth information and to provide opportunity to stay up to date with these approaches and their utility.

CONCLUSIONS

The chosen approaches-genome sequencing, genome-wide association studies, epigenomics, and gene expression-are extremely valuable approaches for collecting research data to help us better understand the pathophysiology of a variety of health-related conditions, but they are also gaining in utility for clinical assessment and testing purposes.

CLINICAL RELEVANCE

Our increased understanding of the molecular underpinnings of disease will assist with better development of screening tests, diagnostic tests, tests that allow us to prognosticate, tests that allow for individualized treatments, and tests to facilitate post-treatment surveillance.

Molecular genomic research designs

Genetic and genomic research approaches have the capability to expand our understanding of the complex pathophysiology of disease susceptibility, susceptibility to complications related to disease, trajectory of recovery from acquired injuries and infections, patient response to interventions and therapeutics, as well as informing diagnoses and prognoses. Nurse scientists are actively involved in all of these fields of inquiry, and the goal of this chapter is to assist with incorporation of genetic and genomic trajectories into their research and facilitate the design and execution of these studies. New studies that are going to embark on recruitment, phenotyping, and sample collection will benefit from forethought about research design to ensure that it addresses the research questions or hypotheses being tested. Studies that will use existing data or samples will also benefit from forethought about research design for the same reason but will also address the fact that some designs may not be feasible with the available data or samples. This chapter discusses candidate gene association, genome-wide association, candidate gene expression, global gene expression, and epigenetic/epigenomic study designs. Information provided includes rationale for selecting an appropriate study design, important methodology considerations for each design, key technologies available to accomplish each type of study, and online resources available to assist in executing each type of study design.

Pooled sample-based GWAS: a cost-effective alternative for identifying colorectal and prostate cancer risk variants in the Polish population

BACKGROUND

Prostate cancer (PCa) and colorectal cancer (CRC) are the most commonly diagnosed cancers and cancer-related causes of death in Poland. To date, numerous single nucleotide polymorphisms (SNPs) associated with susceptibility to both cancer types have been identified, but their effect on disease risk may differ among populations.

METHODS

To identify new SNPs associated with PCa and CRC in the Polish population, a genome-wide association study (GWAS) was performed using DNA sample pools on Affymetrix Genome-Wide Human SNP 6.0 arrays. A total of 135 PCa patients and 270 healthy men (PCa sub-study) and 525 patients with adenoma (AD), 630 patients with CRC and 690 controls (AD/CRC sub-study) were included in the analysis. Allele frequency distributions were compared with t-tests and χ(2)-tests. Only those significantly associated SNPs with a proxy SNP (p<0.001; distance of 100 kb; r(2)>0.7) were selected. GWAS marker selection was conducted using PLINK. The study was replicated using extended cohorts of patients and controls. The association with previously reported PCa and CRC susceptibility variants was also examined. Individual patients were genotyped using TaqMan SNP Genotyping Assays.

RESULTS

The GWAS selected six and 24 new candidate SNPs associated with PCa and CRC susceptibility, respectively. In the replication study, 17 of these associations were confirmed as significant in additive model of inheritance. Seven of them remained significant after correction for multiple hypothesis testing. Additionally, 17 previously reported risk variants have been identified, five of which remained significant after correction.

CONCLUSION

Pooled-DNA GWAS enabled the identification of new susceptibility loci for CRC in the Polish population. Previously reported CRC and PCa predisposition variants were also identified, validating the global nature of their associations. Further independent replication studies are required to confirm significance of the newly uncovered candidate susceptibility loci.

Genetics of temporal lobe epilepsy: a review

Temporal lobe epilepsy (TLE) is usually regarded as a polygenic and complex disorder. To understand its genetic component, numerous linkage analyses of familial forms and association studies of cases versus controls have been conducted since the middle of the nineties. The present paper lists genetic findings for TLE from the initial segregation analysis to the most recent results published in May 2011. To date, no genes have been clearly related to TLE despite many efforts to do so. However, it is vital to continue replication studies and collaborative attempts to find significant results and thus determine which gene variant combination plays a definitive role in the aetiology of TLE.

Breed-specific ancestry studies and genome-wide association analysis highlight an association between the MYH9 gene and heat tolerance in Alaskan sprint racing sled dogs

Alaskan sled dogs are a genetically distinct population shaped by generations of selective interbreeding with purebred dogs to create a group of high-performance athletes. As a result of selective breeding strategies, sled dogs present a unique opportunity to employ admixture-mapping techniques to investigate how breed composition and trait selection impact genomic structure. We used admixture mapping to investigate genetic ancestry across the genomes of two classes of sled dogs, sprint and long-distance racers, and combined that with genome-wide association studies (GWAS) to identify regions that correlate with performance-enhancing traits. The sled dog genome is enhanced by differential contributions from four non-admixed breeds (Alaskan Malamute, Siberian Husky, German Shorthaired Pointer, and Borzoi). A principal components analysis (PCA) of 115,000 genome-wide SNPs clearly resolved the sprint and distance populations as distinct genetic groups, with longer blocks of linkage disequilibrium (LD) observed in the distance versus sprint dogs (7.5-10 and 2.5-3.75 kb, respectively). Furthermore, we identified eight regions with the genomic signal from either a selective sweep or an association analysis, corroborated by an excess of ancestry when comparing sprint and distance dogs. A comparison of elite and poor-performing sled dogs identified a single region significantly associated with heat tolerance. Within the region we identified seven SNPs within the myosin heavy chain 9 gene (MYH9) that were significantly associated with heat tolerance in sprint dogs, two of which correspond to conserved promoter and enhancer regions in the human ortholog.