Replication in Genome-Wide Association Studies

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication, issues of heterogeneity, advantages and disadvantages of different methods of data synthesis across multiple studies, frequentist vs. Bayesian inferences for replication, and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.Comment: Published in at http://dx.doi.org/10.1214/09-STS290 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Characterisation of the genomic architecture of human chromosome 17q and evaluation of different methods for haplotype block definition

BACKGROUND: The selection of markers in association studies can be informed through the use of haplotype blocks. Recent reports have determined the genomic architecture of chromosomal segments through different haplotype block definitions based on linkage disequilibrium (LD) measures or haplotype diversity criteria. The relative applicability of distinct block definitions to association studies, however, remains unclear. We compared different block definitions in 6.1 Mb of chromosome 17q in 189 unrelated healthy individuals. Using 137 single nucleotide polymorphisms (SNPs), at a median spacing of 15.5 kb, we constructed haplotype block maps using published methods and additional methods we have developed. Haplotype tagging SNPs (htSNPs) were identified for each map. RESULTS: Blocks were found to be shorter and coverage of the region limited with methods based on LD measures, compared to the method based on haplotype diversity. Although the distribution of blocks was highly variable, the number of SNPs that needed to be typed in order to capture the maximum number of haplotypes was consistent. CONCLUSION: For the marker spacing used in this study, choice of block definition is not important when used as an initial screen of the region to identify htSNPs. However, choice of block definition has consequences for the downstream interpretation of association study results

An Evaluation of Statistical Approaches to Rare Variant Analysis in Genetic Association Studies

Genome-wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large-scale re-sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population-based genetic studies when data are available from re-sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re-sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley-Liss, Inc

Synthetic associations in the context of genome-wide association scan signals

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with complex traits, but these only explain a small proportion of the total heritability. It has been recently proposed that rare variants can create ‘synthetic association' signals in GWAS, by occurring more often in association with one of the alleles of a common tag single nucleotide polymorphism. While the ultimate evaluation of this hypothesis will require the completion of large-scale sequencing studies, it is informative to place it in the broader context of what is known about the genetic architecture of complex disease. In this review, we draw from empirical and theoretical data to summarize evidence showing that synthetic associations do not underlie many reported GWAS associations

Identification of novel putative rheumatoid arthritis susceptibility genes via analysis of rare variants

Established loci for rheumatoid arthritis (RA), including HLA-DRB1 and PTPN22, do not fully account for the genetic component of susceptibility to the disease. One possible source of as yet undiscovered susceptibility genes are those mediated through effects of rare variants. We present a novel method for gene-based genome-wide scans of whole-genome association (WGA) data to identify accumulations of rare variants associated with disease. We apply our method to WGA SNP genotype data obtained from 868 RA cases and 1194 controls. Our results highlight novel putative RA susceptibility genes that have not previously been identified in large-scale WGA studies

Interrogating Type 2 Diabetes Genome-Wide Association Data Using a Biological Pathway-Based Approach

OBJECTIVE-Recent genome-wide association Studies have resulted in a dramatic increase in our knowledge of the genetic loci involved in type 2 diabetes. In a complementary approach to these single-marker studies, we attempted to identify biological pathways associated with type 2 diabetes. This approach could allow its to identify additional risk loci. RESEARCH DESIGN AND METHODS-We used individual level genotype data generated from the Wellcome Trust Case Control Consortium (WTCCC) type 2 diabetes study, consisting of 393,143 autosomal SNPs, genotyped across 1,924 case subjects and 2,938 control subjects. We sought additional evidence from summary level data available from the Diabetes Genetics Initiative (DGI) and the Finland-United States Investigation of NIDDM Genetics (FUSION) studies. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm (GSEA). A total of 439 pathways were analyzed from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and BioCarta databases. RESULTS-After correcting for the number of pathways tested, we found no strong evidence for any pathway showing association with type 2 diabetes (top P-adj = 0.31). The candidate WNT-signaling pathway ranked top (nominal P = 0.0007, excluding TCF7L2; P = 0.002), containing a number of promising single gene associations. These include CCND2 (rs11833537; P = 0.003), SMAD3 (rs7178347; P = 0.0006), and PRICKLE1 (rs1796390; P = 0.001), all expressed in the pancreas. CONCLUSIONS-Common variants involved in type 2 diabetes risk are likely to occur in or near genes in multiple pathways. Pathway-based approaches to genome-wide association data may be more Successful for some complex traits than others, depending on the nature of the underlying disease physiology. Diabetes 58:1463-1467, 200

A Powerful Approach to Sub-Phenotype Analysis in Population-Based Genetic Association Studies

The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts

Will the real disease gene please stand up?

A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification