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Age of onset and effect size in genome-wide association studies.

ABSTRACT: Genome-wide association studies (GWAS) have identified many susceptibility loci for complex traits, but have not identified the majority of the genetic contribution to common diseases. We explored whether the magnitude of associations detected in GWAS and, therefore, the likelihood of detecting a significant association for a given sample size, is generally greater for childhood-onset traits (e.g., birth defects) than for traits with onset in adulthood.Data were obtained from the National Human Genome Research Institute Catalog of Published GWAS. Traits were categorized as having an average age of onset in childhood (<18 years, n = 15 traits), early adulthood (18-54 years, n = 32 traits), or late adulthood (?55 years, n = 31 traits). The relationship between age of onset category and the magnitude of significant associations detected by GWAS was assessed using logistic regression.Associations characterized by an odds ratio (OR) ? 1.5 were significantly more common for GWAS of childhood traits than for late adulthood-onset traits after adjustment for several covariates (adjusted OR, 2.55; 95% confidence interval, 1.37-4.73). Results in subgroup analyses using more stringent inclusion criteria (based on sample size, effect size, p value threshold for inclusion, and novel variant-trait associations) were similar.These findings suggest that, on average, marker-trait associations detected in GWAS for traits with young onset may have a larger magnitude of effect than those for traits with adult onset. Therefore, GWAS for young-onset traits, such as birth defects, may be more likely than those for adult-onset traits to identify major genetic risk factors. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.

SUBMITTER: Agopian AJ

PROVIDER: S-EPMC4219508 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Age of onset and effect size in genome-wide association studies.

Agopian A J AJ Eastcott Lisa M LM Mitchell Laura E LE

Birth defects research. Part A, Clinical and molecular teratology 20120829 11

<h4>Background</h4>Genome-wide association studies (GWAS) have identified many susceptibility loci for complex traits, but have not identified the majority of the genetic contribution to common diseases. We explored whether the magnitude of associations detected in GWAS and, therefore, the likelihood of detecting a significant association for a given sample size, is generally greater for childhood-onset traits (e.g., birth defects) than for traits with onset in adulthood.<h4>Methods</h4>Data wer ...[more]

PMID: 22933422

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Project description:Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci.

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Age of onset and effect size in genome-wide association studies.

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Age of onset and effect size in genome-wide association studies.

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