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Literature DB >> 23666935

Assessing genome-wide statistical significance for large p small n problems.

Abstract

Assessing genome-wide statistical significance is an important issue in genetic studies. We describe a new resampling approach for determining the appropriate thresholds for statistical significance. Our simulation results demonstrate that the proposed approach accurately controls the genome-wide type I error rate even under the large p small n situations.

Keywords: experimental cross; genome-wide statistical significance; quantitative trait loci (QTL) mapping; resampling method

Mesh：

Year: 2013 PMID： 23666935 PMCID： PMC3697980 DOI： 10.1534/genetics.113.150896

Source DB: PubMed Journal: Genetics ISSN： 0016-6731 Impact factor: 4.562

5 in total

1. R/qtl: QTL mapping in experimental crosses.

Authors: Karl W Broman; Hao Wu; Saunak Sen; Gary A Churchill
Journal: Bioinformatics Date: 2003-05-01 Impact factor: 6.937

2. An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait Loci.

Authors: Fei Zou; Jason P Fine; Jianhua Hu; D Y Lin
Journal: Genetics Date: 2004-12 Impact factor: 4.562

3. Significance thresholds for quantitative trait locus mapping under selective genotyping.

Authors: Ani Manichaikul; Abraham A Palmer; Saunak Sen; Karl W Broman
Journal: Genetics Date: 2007-08-24 Impact factor: 4.562

4. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results.

Authors: E Lander; L Kruglyak
Journal: Nat Genet Date: 1995-11 Impact factor: 38.330

5. Empirical threshold values for quantitative trait mapping.

Authors: G A Churchill; R W Doerge
Journal: Genetics Date: 1994-11 Impact factor: 4.562

5 in total

15 in total

1. An efficient method to handle the 'large p, small n' problem for genomewide association studies using Haseman-Elston regression.

Authors: Bujun Mei; Zhihua Wang
Journal: J Genet Date: 2016-12 Impact factor: 1.166

2. Efficient methods for signal detection from correlated adverse events in clinical trials.

Authors: Guoqing Diao; Guanghan F Liu; Donglin Zeng; William Wang; Xianming Tan; Joseph F Heyse; Joseph G Ibrahim
Journal: Biometrics Date: 2019-03-29 Impact factor: 2.571

3. EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.

Authors: Xiangju Liu; Yu Zhang; Chunli Fu; Ruochi Zhang; Fengfeng Zhou
Journal: Front Genet Date: 2021-04-27 Impact factor: 4.599

4. The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis.

Authors: Juanying Xie; Mingzhao Wang; Shengquan Xu; Zhao Huang; Philip W Grant
Journal: Front Genet Date: 2021-05-13 Impact factor: 4.599

Assessing genome-wide statistical significance for large p small n problems.

1. R/qtl: QTL mapping in experimental crosses.

2. An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait Loci.

3. Significance thresholds for quantitative trait locus mapping under selective genotyping.

4. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results.

5. Empirical threshold values for quantitative trait mapping.

1. An efficient method to handle the 'large p, small n' problem for genomewide association studies using Haseman-Elston regression.

2. Efficient methods for signal detection from correlated adverse events in clinical trials.

3. EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models.

4. The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis.

5. Using SNP Weights Derived From Gene Expression Modules to Improve GWAS Power for Feed Efficiency in Pigs.

6. McTwo: a two-step feature selection algorithm based on maximal information coefficient.

7. 2D association and integrative omics analysis in rice provides systems biology view in trait analysis.

8. A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

Review 9. Understanding the Molecular Mechanisms of Asthma through Transcriptomics.

10. AgeGuess, a Methylomic Prediction Model for Human Ages.