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Open Access Research article

Comparison of similarity-based tests and pooling strategies for rare variants

Sergii Zakharov12*, Agus Salim2 and Anbupalam Thalamuthu1*

Author Affiliations

1 Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, Singapore 138672, Singapore

2 Saw Swee Hock School of Public Health, National University of Singapore, 16 Medical Drive, Singapore 117597, Singapore

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BMC Genomics 2013, 14:50  doi:10.1186/1471-2164-14-50

Published: 24 January 2013

Additional files

Additional file 1:

Empirical type-1 error estimate for population genetics simulations (Table S1), detailed description of population genetics simulations, and considerations for possible reasons for MDMR power loss when applied with weighting pooling strategy.

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Open Data

Additional file 2:

Power as a function of significance level for the four similarity-based tests with IBS kernels and two rare variants pooling strategies. Panel 1: “Risk Rare” Scenario; Panel 2: “Risk Both” Scenario; Panel 3: “Risk Common” Scenario; Panel 4: “Mixed Rare” Scenario.

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Open Data

Additional file 3:

Empirical type-1 error rates for dichotomized adjusted quantitative phenotype in GAW17 data set at the theoretical level of 0.05 (ARNT-VEGFC with Q1, and BCHE-VWF with Q2).

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Open Data

Additional file 4:

Empirical type-1 error rates for dichotomized adjusted case–control status in GAW17 data set at the theoretical level of 0.05.

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Open Data

Additional file 5:

Power to identify an association with dichotomized adjusted case–control status in GAW17 data set for some of the causal genes.

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Open Data

Additional file 6:

Power to identify an association with dichotomized adjusted case–control status in GAW17 data set for some of the causal genes.

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Open Data

Additional file 7:

Impact of power value on MDMR test performance in a “Risk Rare” scenario.

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Open Data