The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases.
paper
Cited
Public
- Authors
- Manchia, Mirko; Cullis, Jeffrey; Turecki, Gustavo; Rouleau, Guy A; Uher, Rudolf; Alda, Martin
- Year
- 2013
- Journal
- PloS one
- PMID
- 24146854
- DOI
- 10.1371/journal.pone.0076295
- PMCID
- PMC3795757
Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of "non-cases") reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.
The impact of heterogeneity on the sample size (cases and controls) required for 90% of statistical power.The minimum sample size to achieve to detect association was calculated in simulated case-control data with increasing proportion of βnon-casesβ considering a disease prevalence of 0.01. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk.
LLM interpretation
This figure consists of six line graphs (panels A-F) showing the relationship between the proportion of "non-cases" (x-axis) and the sample size required for 90% statistical power (y-axis). Across all panels, the required sample size increases exponentially as the proportion of "non-cases" increases. Different colored lines represent various minor allele frequencies (MAF: 0.01 black, 0.05 grey, 0.2 red, 0.5 blue), with the required sample size generally decreasing as the relative risk (RR = 1.2, 1.5, 2) increases. Panels A, C, and E represent dominant genetic models, while B, D, and F represent multiplicative models.
The impact of heterogeneity on the estimation of the genetic effect size.Odds ratios from simulated case-control data were calculated for each step of admixture. Data are reported for minor allele frequencies (MAF) of 0.01 (black), 0.05 (grey), 0.2 (red) and 0.5 (blue). The results are reported for dominant (panels A, C, and E) and multiplicative (panels B, D, and F) genetic models. RR = relative risk; OR = odds ratio.
LLM interpretation
This figure consists of six line graphs (panels A-F) showing the relationship between the proportion of "non-cases" (x-axis) and the odds ratio (OR, y-axis) across different relative risks (RR = 1.2, 1.5, and 2). Each panel contains four colored lines representing different minor allele frequencies (MAF): 0.01 (black), 0.05 (grey), 0.2 (red), and 0.5 (blue). Across all panels, the OR decreases as the proportion of "non-cases" increases, with the divergence between MAF groups becoming more pronounced at higher relative risks (RR = 2).
Genome-wide analysis of the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes type 1 (T1D) and type 2 (T2D) under heterogeneity: twenty most significant associations [βlog(p-values)].SNP = single nucleotide polymorphism; Ξ² = admixture.
LLM interpretation
This figure consists of two tables presenting genome-wide association data for Type 1 Diabetes (T1D) and Type 2 Diabetes (T2D) from the WTCCC dataset. Each table lists the twenty most significant SNPs, comparing reported p-values against genotype p-values and p-values calculated under varying levels of admixture ($\beta$) from 0% to 100%. The data shows a general trend where $-\log(p\text{-values})$ decrease as the admixture percentage increases, with corresponding significance levels indicated in the bottom row.
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| Genetic modifiers of Mendelian disease: Huntington's disease and the trinucleotide repeat disorders. | Holmans PA et al. | β | 2017 | β |
| Genetics of HIV-associated sensory neuropathy and related pain in Africans. | Ngassa Mbenda HG et al. | β | 2017 | β |
| Genome-Wide Associations Related to Hepatic Histology in Nonalcoholic Fatty Liver Disease in Hispanic Boys. | Wattacheril J et al. | β | 2017 | β |
| HLA-DRB1*15:01 and HLA-DRB3*02:02 in PLA2R-Related Membranous Nephropathy. | Le WB et al. | β | 2017 | β |
| Prevalence of juvenile myoclonic epilepsy in people <30 years of age-A population-based study in Norway. | Syvertsen M et al. | β | 2017 | β |
| Psychiatric genetics - Does diagnosis matter? | Alda M | β | 2017 | β |
| The clinical trajectory of emerging bipolar disorder among the high-risk offspring of bipolar parents: current understanding and future considerations. | Duffy A et al. | β | 2017 | β |
| The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies. | Schrodi SJ | β | 2017 | β |
| A genome-wide association study of bipolar disorder with comorbid eating disorder replicates the SOX2-OT region. | Liu X et al. | β | 2016 | β |
| Analysis of binary responses with outcome-specific misclassification probability in genome-wide association studies. | Rekaya R et al. | β | 2016 | β |
| Assessment and characterization of phenotypic heterogeneity of anxiety disorders across five large cohorts. | Lee M et al. | β | 2016 | β |
| Childhood maltreatment and comorbid anxiety in people with bipolar disorder. | Pavlova B et al. | β | 2016 | β |
| Cross-Disorder Genetic Analysis of Tic Disorders, Obsessive-Compulsive, and Hoarding Symptoms. | ZilhΓ£o NR et al. | β | 2016 | β |
| Eating disorders: What age at onset? | Volpe U et al. | β | 2016 | β |
| Exploring possible association between DΞ²H genotype (C1021T), early onset of conduct disorder and psychopathic traits in juvenile delinquents. | Isaksson J et al. | β | 2016 | β |
| Genetic assessment of additional endophenotypes from the Consortium on the Genetics of Schizophrenia Family Study. | Greenwood TA et al. | β | 2016 | β |
| Genetic Association-Guided Analysis of Gene Networks for the Study of Complex Traits. | Greene CS et al. | β | 2016 | β |
| Genetic-risk assessment of GWAS-derived susceptibility loci for type 2 diabetes in a 10 year follow-up of a population-based cohort study. | Go MJ et al. | β | 2016 | β |
| Genome-wide association studies in pediatric chronic kidney disease. | Gupta J et al. | β | 2016 | β |
| Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. | Hou L et al. | β | 2016 | β |
| Genome-wide association study of lifetime cannabis use based on a large meta-analytic sample of 32 330 subjects from the International Cannabis Consortium. | Stringer S et al. | β | 2016 | β |
| Increased Nigral SLC6A3 Activity in Schizophrenia Patients: Findings From the Toronto-McLean Cohorts. | Kennedy JL et al. | β | 2016 | β |
| Polygenic associations of neurodevelopmental genes in suicide attempt. | Sokolowski M et al. | β | 2016 | β |
| Polygenic dissection of major depression clinical heterogeneity. | Milaneschi Y et al. | β | 2016 | β |
| Population-based approaches to genetics of migraine. | Chasman DI et al. | β | 2016 | β |
| Quantitative magnetic resonance imaging traits as endophenotypes for genetic mapping in epilepsy. | Alhusaini S et al. | β | 2016 | β |
| Semi-supervised learning of the electronic health record for phenotype stratification. | Beaulieu-Jones BK et al. | β | 2016 | β |
| TESTING POPULATION-SPECIFIC QUANTITATIVE TRAIT ASSOCIATIONS FOR CLINICAL OUTCOME RELEVANCE IN A BIOREPOSITORY LINKED TO ELECTRONIC HEALTH RECORDS: LPA AND MYOCARDIAL INFARCTION IN AFRICAN AMERICANS. | Dumitrescu L et al. | β | 2016 | β |
| The genetics of breast cancer risk in the post-genome era: thoughts on study design to move past BRCA and towards clinical relevance. | Skol AD et al. | β | 2016 | β |
| A novel relationship for schizophrenia, bipolar and major depressive disorder Part 7: A hint from chromosome 7 high density association screen. | Chen X et al. | β | 2015 | β |
| Developmental differences in early adolescent aggression: a geneΒ ΓΒ environmentΒ ΓΒ intervention analysis. | Schlomer GL et al. | β | 2015 | β |
| Does nature have joints worth carving? A discussion of taxometrics, model-based clustering and latent variable mixture modeling. | Lubke GH et al. | β | 2015 | β |
| Genome-Wide Association Studies for Taxane-Induced Peripheral Neuropathy in ECOG-5103 and ECOG-1199. | Schneider BP et al. | β | 2015 | β |
| Genomic architecture of asthma differs by sex. | Mersha TB et al. | β | 2015 | β |
| Heritability of liver enzyme levels estimated from genome-wide SNP data. | van Beek JH et al. | β | 2015 | β |
| Homogeneous case subgroups increase power in genetic association studies. | Traylor M et al. | β | 2015 | β |
| Looking Forward in Candidate Gene Research: Concerns and Suggestions. | Schlomer GL et al. | β | 2015 | β |
| Neuropathic pain phenotyping by international consensus (NeuroPPIC) for genetic studies: a NeuPSIG systematic review, Delphi survey, and expert panel recommendations. | van Hecke O et al. | β | 2015 | β |
| Regulatory Rewiring in a Cross Causes Extensive Genetic Heterogeneity. | Matsui T et al. | β | 2015 | β |
| Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model. | Shin J et al. | β | 2015 | β |
| The impact of clinical heterogeneity in schizophrenia on genomic analyses. | Liang SG et al. | β | 2015 | β |
| The Importance of Measuring Multi-level Risk and Illness Progression Markers in High-risk Youth From Well-characterized Bipolar Parents. | Duffy A | β | 2015 | β |
| Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it? | Levinson DF et al. | β | 2014 | β |
| Multivariate genetic analyses in heterogeneous populations. | Lubke G et al. | β | 2014 | β |
| Personalized medicine for chronic, complex diseases: chronic obstructive pulmonary disease as an example. | Radder JE et al. | β | 2014 | β |
| Race, class, and AKI. | Demirjian S | β | 2014 | β |
| The effect of FTO rs9939609 on major depression differs across MDD subtypes. | Milaneschi Y et al. | β | 2014 | β |