Sample Size for Successful Genome-Wide Association Study of Major Depressive Disorder
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Abstract:
Major depressive disorder (MDD) is a complex, heritable psychiatric disorder. Advanced statistical genetics for genome-wide association studies (GWASs) have suggested that the heritability of MDD is largely explained by common single nucleotide polymorphisms (SNPs). However, until recently, there has been little success in identifying MDD-associated SNPs. Here, based on an empirical Bayes estimation of a semi-parametric hierarchical mixture model using summary statistics from GWASs, we show that MDD has a distinctive polygenic architecture consisting of a relatively small number of risk variants (~17%), e.g., compared to schizophrenia (~42%). In addition, these risk variants were estimated to have very small effects (genotypic odds ratio ≤ 1.04 under the additive model). Based on the estimated architecture, the required sample size for detecting significant SNPs in a future GWAS was predicted to be exceptionally large. It is noteworthy that the number of genome-wide significant MDD-associated SNPs would rapidly increase when collecting 50,000 or more MDD-cases (and the same number of controls); it can reach as much as 100 SNPs out of nearly independent (linkage disequilibrium pruned) 100,000 SNPs for ~120,000 MDD-cases.Keywords:
Genome-wide Association Study
Linkage Disequilibrium
Genetic architecture
Genetic Association
This brief review provides a summary of the biological causes of genetic association between tightly linked markers – termed linkage disequilibrium – and unlinked markers – termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.
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Summary Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects.
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Directional selection produces a change in the population mean of a phenotype, but this shift will only be translated into a phenotypic difference between generations if the trait is heritable, in the sense that there is additive genetic variance in the trait. Thus any study of evolutionary change must concern itself both with the factors causing phenotypic change and the genetic architecture of the traits under investigation. In the case of a single trait the relevant genetic parameter is the narrow sense heritability, de®ned as the ratio of the additive genetic variance to the phenotypic variance (Falconer, 1989). Heritability can be estimated by pedigree analysis (e.g. full-sibs, half-sibs) or by response to selection, the latter estimate frequently being called the realized heritability. Estimation of the heritability from pedigree analysis is the more generally adopted method, because it is typically both less labour-intensive and quicker, requiring only one or two generations of breeding. A selection experiment is not simply an alternative method of estimating heritability but can provide insights into the genetic architecture of a trait that are not evident from the sib analysis. For example, according to the standard response equation (Response Heritability
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This chapter contains sections titled: Introduction Linkage Disequilibrium Mapping Genes Using Linkage Disequilibrium Tests for Association Analysis of Haplotype Data Association Tests for Quantitative Traits Association and Genomic Screening Special Populations Summary References
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Abstract Complex diseases affect a substantial proportion of the human population and are caused by multiple genetic and environmental effects. The association study is a means of identifying genetic variation that may be involved in complex disease etiology. The existence of linkage disequilibrium (nonrandom association of alleles) across the human genome can be used to reduce the number of variants needed to successfully correlate with phenotypic traits. We discuss the current status and problems inherent in performing whole genome association studies to analyze complex diseases.
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Abstract Genome‐wide association studies (GWASs) have identified thousands of genetic variants involved in complex traits and diseases, but these only explain a minor fraction of the heritability. New methodologies that scrutinise the GWAS data indicate where the missing heritability might be found. About half of the heritability is still hidden in the GWAS data as this concerns common variants with small effects. Furthermore, a large part is still missing because this involves rare variants, which cannot be detected by GWAS due to low linkage disequilibrium. Nevertheless, even estimates of the most sophisticated methods do not fully reach the total genetic contributions derived from twin and family studies, suggesting that these heritabilities may be overestimated due to violation of the underlying assumptions or heterogeneity in heritability estimation across populations. Future variant discovery for complex traits and diseases will capture an ever larger part of the genetic predisposition and eventually bring health care applications within reach. Key Concepts Genome‐wide association studies (GWAS) have so far been able to only explain a minority of the heritability of complex traits or diseases, a large part is missing. The missing heritability can be divided in hidden heritability, still‐missing heritability and phantom heritability. Part of the missing heritability is hidden in the GWAS data meaning that due to lack of power the underlying common genetic variants have not yet been identified. About half of the heritability of complex traits or diseases is expected to be caused by common variants and can ultimately be found by GWAS. The still‐missing heritability can be explained by rare and structural genetic variants, dominance effects and epistasis. The effects of dominance and epistasis are likely not very large. Heritability estimates from twin and family studies may be overestimated due to violation of underlying model assumptions and therefore cause phantom heritability. Heritability is likely also missing due to heterogeneity of effects between and within populations, as estimates from twin and family studies are calculated in homogeneous populations.
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Abstract Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from ENIGMA consortium’s genome-wide association study (GWAS) of regional brain volume, we sought to test whether there is shared genetic architecture between 8 subcortical brain volumes and MDD. Using LD score regression utilising summary statistics from ENIGMA and the Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (r G =0.46, P =0.02), although this did not survive multiple comparison testing. None of other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. We also generated polygenic risk scores (PRS) to assess potential pleiotropy between regional brain volumes and MDD in three cohorts (Generation Scotland; Scottish Family Health Study (n=19,762), UK Biobank (n=24,048) and the English Longitudinal Study of Ageing (n=5,766). We used logistic regression to examine volumetric PRS and MDD and performed a meta-analysis across the three cohorts. No regional volumetric PRS demonstrated significant association with MDD or recurrent MDD. In this study we provide some evidence that hippocampal volume and MDD may share genetic architecture, albeit this did not survive multiple testing correction and was in the opposite direction to most reported phenotypic correlations. We therefore found no evidence to support a shared genetic architecture for MDD and regional subcortical volumes.
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This chapter considers allelic association to be association occurring between alleles at different loci. Allelic association can occur between linked or unlinked loci. There are many different measures of linkage disequilibrium. Studies examining association between single-nucleotide polymorphisms (SNPs) have demonstrated that linkage disequilibrium may be organized in distinct regions of strong linkage disequilibrium, with little association occurring between SNPs in different regions. Association tests with quantitative traits use continuous measures, such as weight or height, and test the correlation between genotypes and the trait values in the sample. Case–control studies can provide estimates of important measures of association and impact. Family-based studies ensure that cases and controls are appropriately matched by using family-based controls. Testing for association between genetic markers and a quantitative trait can be used in fine-mapping the quantitative trait loci in the same way that we use association tests to look for genes for dichotomous traits.
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