Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 300 SNPs.

Nikita A. Kuznetsov (24651)
Christopher K. Rhea (354565)

March 2017

(A-B) These panels depict the expected power for between- and within-subject designs using 100 st...

Joost van Heerwaarden (304387)
Martijn van Zanten (72709)
Willem Kruijer (817778)

October 2015

The proportion of simulated traits for which the -log10(p) value of the causal SNP is above the t...

Estimated variance explained and power as a function of non-additivity measure τ´ in regions of 100 SNPs simulated from 1000 genome data.

Guillaume Paré (84081)
Senay Asma (719601)
Wei Q. Deng (719602)

April 2015

Regions of 100 SNPs were simulated from phased 1000 Genomes data in 5,000 individuals, excluding ...

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 30 SNPs.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

For each effect size (1,2,3,4,5 or 10% of the total variance) 100 simulations were carried out. T...

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 10 SNPs.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

For each effect size (1,2,3,4,5 or 10% of the total variance) 100 simulations were carried out. T...

The mean power is plotted against variance explained for the number of SNPs the effect is spread over.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

As indicated, the total amount of variance explained, rather than the number of SNPs the effect i...

Type S error rate (i.e. published estimates with the wrong sign) as a function of the prevalence of true effects, statistical power and bias when different effect sizes are used for power calculations.

Kleber Neves (9268080)
Pedro B. Tan (9710299)
Olavo B. Amaral (7379459)

December 2022

In (A), sample size is calculated to detect the mean of the true effect sizes above the minimum of i...

PPV as a function of the prevalence of true effects, statistical power and bias for the single normal model when different effect sizes are used for power calculations.

Kleber Neves (9268080)
Pedro B. Tan (9710299)
Olavo B. Amaral (7379459)

December 2022

In (A), sample size is calculated to detect the mean of the true effect sizes above the minimum of i...

Standard error of the estimate of variance explained by all SNPs vs. sample size.

Peter M. Visscher (16568)
Gibran Hemani (208828)
Anna A. E. Vinkhuyzen (548307)
Guo-Bo Chen (228757)
Sang Hong Lee (77173)
Naomi R. Wray (127161)
Michael E. Goddard (252450)
Jian Yang (12136)

April 2014

The first three columns are the averaged standard error observed from 100 simulations under three...

Simulated power under the same simulation scenarios of [7] over 1,000 replicates.

Kai Wang (21246)

September 2014

In scenario I, only phenotype 1 is associated with the SNP. rG = 1 or −1 does not affect t...

Impact of effect size, sample size and minor allele frequency on power.

Jiemin Liao (514238)
Xiang Li (114679)
Tien-Yin Wong (159436)
Jie Jin Wang (103779)
Chiea Chuen Khor (306345)
E. Shyong Tai (103763)
Tin Aung (103674)
Yik-Ying Teo (159446)
Ching-Yu Cheng (159319)

January 2014

Measurement error is displayed in terms of the number of SD of the true phenotype (without errors...

Simulation results for a range of shift parameters and proportions of SNPs included within a gene.

Andrea S. Foulkes (170604)
Gregory J. Matthews (279755)
Ujjwal Das (5672)
Jane F. Ferguson (279756)
Rongheng Lin (263648)
Muredach P. Reilly (206360)

February 2013

The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...

Simulation results for using different weights to construct SNP set tests.

Xingjie Hao (4803879)
Ping Zeng (391808)
Shujun Zhang (314599)
Xiang Zhou (164107)

January 2018

(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simu...

Statistical powers of all the simulated QTNs in the first simulation experiment plotted against Type 1 error (in a log10 scale) for the five GWAS methods (ISIS EM-BLASSO, SCAD, EMMA, FarmCPU, and mrMLM).

Cox Lwaka Tamba (3699835)
Yuan-Li Ni (3699832)
Yuan-Ming Zhang (42129)

January 2017

Statistical powers of all the simulated QTNs in the first simulation experiment plotted against T...

Power analysis results (mixture contamination model).

Mattias Rantalainen (166786)
Cecilia M. Lindgren (145706)
Christopher C. Holmes (29850)

May 2015

A) Power as a function of contamination proportion. B) Power as a function of study size. C) Powe...

Simulation results.

Nikita A. Kuznetsov (24651)
Christopher K. Rhea (354565)

March 2017

(A-B) These panels depict the expected power for between- and within-subject designs using 100 st...

Power in simulations.

Joost van Heerwaarden (304387)
Martijn van Zanten (72709)
Willem Kruijer (817778)

October 2015

The proportion of simulated traits for which the -log10(p) value of the causal SNP is above the t...

Estimated variance explained and power as a function of non-additivity measure τ´ in regions of 100 SNPs simulated from 1000 genome data.

Guillaume Paré (84081)
Senay Asma (719601)
Wei Q. Deng (719602)

April 2015

Regions of 100 SNPs were simulated from phased 1000 Genomes data in 5,000 individuals, excluding ...

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 30 SNPs.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

For each effect size (1,2,3,4,5 or 10% of the total variance) 100 simulations were carried out. T...

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 10 SNPs.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

For each effect size (1,2,3,4,5 or 10% of the total variance) 100 simulations were carried out. T...

The mean power is plotted against variance explained for the number of SNPs the effect is spread over.

W. David Hill (534895)
Christiaan de Leeuw (457984)
Gail Davies (107670)
David Cherry McLachlan Liewald (534896)
Anthony Payton (534897)
Leone C. A. Craig (534898)
Lawrence J. Whalley (231873)
Mike Horan (534899)
William Ollier (107701)
John M. Starr (107707)
Neil Pendleton (107711)
Danielle Posthuma (237783)
Timothy C. Bates (534900)
Ian J. Deary (107724)

March 2014

As indicated, the total amount of variance explained, rather than the number of SNPs the effect i...

Type S error rate (i.e. published estimates with the wrong sign) as a function of the prevalence of true effects, statistical power and bias when different effect sizes are used for power calculations.

Kleber Neves (9268080)
Pedro B. Tan (9710299)
Olavo B. Amaral (7379459)

December 2022

In (A), sample size is calculated to detect the mean of the true effect sizes above the minimum of i...

PPV as a function of the prevalence of true effects, statistical power and bias for the single normal model when different effect sizes are used for power calculations.

Kleber Neves (9268080)
Pedro B. Tan (9710299)
Olavo B. Amaral (7379459)

December 2022

In (A), sample size is calculated to detect the mean of the true effect sizes above the minimum of i...

Standard error of the estimate of variance explained by all SNPs vs. sample size.

Peter M. Visscher (16568)
Gibran Hemani (208828)
Anna A. E. Vinkhuyzen (548307)
Guo-Bo Chen (228757)
Sang Hong Lee (77173)
Naomi R. Wray (127161)
Michael E. Goddard (252450)
Jian Yang (12136)

April 2014

The first three columns are the averaged standard error observed from 100 simulations under three...

Simulated power under the same simulation scenarios of [7] over 1,000 replicates.

Kai Wang (21246)

September 2014

In scenario I, only phenotype 1 is associated with the SNP. rG = 1 or −1 does not affect t...

Impact of effect size, sample size and minor allele frequency on power.

Jiemin Liao (514238)
Xiang Li (114679)
Tien-Yin Wong (159436)
Jie Jin Wang (103779)
Chiea Chuen Khor (306345)
E. Shyong Tai (103763)
Tin Aung (103674)
Yik-Ying Teo (159446)
Ching-Yu Cheng (159319)

January 2014

Measurement error is displayed in terms of the number of SD of the true phenotype (without errors...

Simulation results for a range of shift parameters and proportions of SNPs included within a gene.

Andrea S. Foulkes (170604)
Gregory J. Matthews (279755)
Ujjwal Das (5672)
Jane F. Ferguson (279756)
Rongheng Lin (263648)
Muredach P. Reilly (206360)

February 2013

The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...

Simulation results for using different weights to construct SNP set tests.

Xingjie Hao (4803879)
Ping Zeng (391808)
Shujun Zhang (314599)
Xiang Zhou (164107)

January 2018

(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simu...

Statistical powers of all the simulated QTNs in the first simulation experiment plotted against Type 1 error (in a log10 scale) for the five GWAS methods (ISIS EM-BLASSO, SCAD, EMMA, FarmCPU, and mrMLM).

Cox Lwaka Tamba (3699835)
Yuan-Li Ni (3699832)
Yuan-Ming Zhang (42129)

January 2017

Statistical powers of all the simulated QTNs in the first simulation experiment plotted against T...

Power analysis results (mixture contamination model).

Mattias Rantalainen (166786)
Cecilia M. Lindgren (145706)
Christopher C. Holmes (29850)

May 2015

A) Power as a function of contamination proportion. B) Power as a function of study size. C) Powe...

Simulation results.

Nikita A. Kuznetsov (24651)
Christopher K. Rhea (354565)

March 2017

(A-B) These panels depict the expected power for between- and within-subject designs using 100 st...

Power in simulations.

Joost van Heerwaarden (304387)
Martijn van Zanten (72709)
Willem Kruijer (817778)

October 2015

The proportion of simulated traits for which the -log10(p) value of the causal SNP is above the t...

Estimated variance explained and power as a function of non-additivity measure τ´ in regions of 100 SNPs simulated from 1000 genome data.

Guillaume Paré (84081)
Senay Asma (719601)
Wei Q. Deng (719602)

April 2015

Regions of 100 SNPs were simulated from phased 1000 Genomes data in 5,000 individuals, excluding ...

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 300 SNPs.

Abstract

Extracted data

Simulations exploring how power fuctuates as a function of total variance explained when the effect is spread over 300 SNPs.

Abstract

Extracted data

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