Use of phenotypic covariates in association analysis by sequential addition of cases

MacGregor, Stuart; Craddock, Nicholas John; Holmans, Peter Alan

doi:10.1038/sj.ejhg.5201604

Cited by 22 publications

(29 citation statements)

References 19 publications

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“…We propose a two-stage strategy which firstly analyses the trait within cases as a continuous measure, and secondly a series of case-control analyses where case subsets are defined using both increasing and decreasing trait values (e.g., late onset and early onset depression cases). A genome-wide analysis of these defined subsets at a coarse grid of quantitative trait values provides a good screening analysis, and interesting results can be followed up in a case-control analysis at a fine-scale for defining trait subsets in cases, using the Sequential Addition method [Macgregor et al, 2006]. The time-to-event analysis provided results that were highly correlated with the full case-control analysis, and was not a useful addition to the analysis strategy.…”

Section: Discussionmentioning

confidence: 98%

“…We additionally defined a late-onset cohort with AAO !25 years. Any SNP reaching suggestive evidence of significance (P ¼ 5 Â 10 À6 ) in these age-thresholds case-control analyses was analyzed further using the Sequential Addition method proposed by Macgregor et al [2006]. This method orders cases by AAO and performs a case-control analysis for cases onset at each observed AAO (and under), forming a finer grid analysis than our 5-year interval approach.…”

Section: Methodsmentioning

confidence: 99%

“…SNPs in ten independent genetic regions reached suggestive evidence for association in at least one of the five AAO analyses Table III). The association P-values at each possible age-cut-off are shown in Figure 2 for the top 9 SNPs, using the Sequential Addition methods of Macgregor et al [2006]. The strongest evidence for association was observed at rs2273289 in PLOD1, on chromosome 1 (P ¼ 1.29EÀ07).…”

Section: Thresholds Of Aao Case-control Analysismentioning

confidence: 97%

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Dissecting the Genetic Heterogeneity of Depression Through Age at Onset

Power

Keers

et al. 2012

American J of Med Genetics Pt B

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Genome-wide studies in major depression have identified few replicated associations, potentially due to heterogeneity within the disorder. Several studies have suggested that age at onset (AAO) can distinguish sub-types of depression with specific heritable components. This paper investigates the role of AAO in the genetic susceptibility for depression using genome-wide association data on 2,746 cases and 1,594 screened controls from the RADIANT studies, with replication performed in 1,471 cases and 1,403 controls from two Munich studies. Three methods were used to analyze AAO: First a time-to-event analysis with controls censored, secondly comparing controls to case-subsets defined using AAO cut-offs, and lastly analyzing AAO as a quantitative trait. In the time-to-event analysis three SNPs reached suggestive significance (P < 5E-06), overlapping with the original case-control analysis of this study. In a case-control analysis using AAO thresholds, SNPs in 10 genomic regions showed suggestive association though again none reached genome-wide significance. Lastly, case-only analysis of AAO as a quantitative trait resulted in 5 SNPs reaching suggestive significance. Sex specific analysis was performed as a secondary analysis, yielding one SNP reaching genome-wide significance in early-onset males. No SNPs achieved significance in the replication study after correction for multiple testing. Analysis of AAO as a quantitative trait did suggest that, across all SNPs, common genetic variants explained a large proportion of the variance (51%, P = 0.04). This study provides the first focussed analysis of the genetic contribution to AAO in depression, and establishes a statistical framework that can be applied to a quantitative trait underlying any disorder.

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Section: Discussionmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Thresholds Of Aao Case-control Analysismentioning

confidence: 97%

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Dissecting the Genetic Heterogeneity of Depression Through Age at Onset

Power

Keers

et al. 2012

American J of Med Genetics Pt B

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“…In contrast, our test determines the optimal threshold. Another related method has been proposed in the context of case/control studies by Macgregor et al [2006]: the sequential addition of cases, inspired by the method OSA [Hauser et al, 2004] compares a fixed sample of controls to the subsample of cases with covariate value below (or above, according to whether the test is performed in ascending or descending order) a threshold which is determined to maximize the computed statistic. A transposition of the OTDT in the context of case/ control studies would rather lead to a case only method, comparing two subsamples of cases separated by a threshold of the DRCV; a potential advantage of this method is that it would not depend of the choice of an ascending or descending order for the test.…”

Section: Resultsmentioning

confidence: 99%

The ordered transmission disequilibrium test: detection of modifier genes

Perdry

Babron

Clerget‐Darpoux

2008

Genetic Epidemiology

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We consider the problem of detection of modifier genes that lead to variations in a disease-related continuous variable (DRCV), such as the age of onset or a measure of disease severity, in a strategy of candidate genes. We propose a novel method, the ordered transmission disequilibrium test (OTDT), to test for a relation between the clinical heterogeneity expressed by a DRCV and marker genotypes of a candidate gene. The OTDT applies to trio families with one patients and his parents, all three genotyped at a bi-allelic marker M. The OTDT aims to find a critical value of the DRCV which separates the sample of families in two subsamples in which the transmission rates are significantly different. We investigate the power of the method by simulations under various genetic models and covariate distributions and compare it with a linear regression analysis.

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“…There have been a few major works that deal with data partitioning according to a phenotypic variable. [9][10][11] These works perform data stratification (akin a screening step proposed in our paper), mostly deal with discrete traits and do not propose a generative model of the data.…”

Section: Introductionmentioning

confidence: 99%

Mixture Model for Sub-Phenotyping in Gwas

et al. 2011

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Genome Wide Association (GWA) studies resulted in discovery of genetic variants underlying several complex diseases including Chron's disease and age-related macular degeneration (AMD). Still geneticists find that in majority of studies the size of the effect even if it is significant tends to be very small. There are several factors contributing to this problem such as rare variants, complex relationships among SNPs (epistatic effect), and heterogeneity of the phenotype. In this work we focus on addressing phenotypic heterogeneity. We introduce the problem of identifying, from GWAS data, separate genotypic markers from overlapping mixtures of clinically indistinguishable phenotypes. We propose a generative model for this scenario and derive an expectation-maximization (EM) procedure to fit the model to data, as well as a novel screening procedure designed to identify skew specific to certain phenotypic regimes. We present results on several simulated datasets as well as preliminary findings in applying the model to type 2 diabetes dataset.

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Use of phenotypic covariates in association analysis by sequential addition of cases

Cited by 22 publications

References 19 publications

Dissecting the Genetic Heterogeneity of Depression Through Age at Onset

Dissecting the Genetic Heterogeneity of Depression Through Age at Onset

The ordered transmission disequilibrium test: detection of modifier genes

Mixture Model for Sub-Phenotyping in Gwas

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