scholarly journals Designing candidate gene and genome-wide case–control association studies

2007 ◽  
Vol 2 (10) ◽  
pp. 2492-2501 ◽  
Author(s):  
Krina T Zondervan ◽  
Lon R Cardon
Keyword(s):  
Candidate Gene ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide ◽  
Control Association

scholarly journals Robust ranks of true associations in genome-wide case-control association studies

2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Gang Zheng ◽  
Jungnam Joo ◽  
Jing-Ping Lin ◽  
Mario Stylianou ◽  
Myron A Waclawiw ◽  
...  
Keyword(s):  
Association Studies ◽  
Case Control ◽  
Genome Wide ◽  
Control Association

scholarly journals Combining case-control status and family history of disease increases association power

2019 ◽  
Author(s):  
Margaux L.A. Hujoel ◽  
Steven Gazal ◽  
Po-Ru Loh ◽  
Nick Patterson ◽  
Alkes L. Price
Keyword(s):  
Family History ◽  
Linear Regression ◽  
Threshold Model ◽  
Association Studies ◽  
Case Control ◽  
Posterior Mean ◽  
Genome Wide ◽  
History Of Disease ◽  
History Of ◽  
Control Association

AbstractFamily history of disease can provide valuable information about an individual’s genetic liability for disease in case-control association studies, but it is currently unclear how to best combine case-control status and family history of disease. We developed a new association method based on posterior mean genetic liabilities under a liability threshold model, conditional on both case-control status and family history (LT-FH); association statistics are computed via linear regression of genotypes and posterior mean genetic liabilities, equivalent to a score test. We applied LT-FH to 12 diseases from the UK Biobank (average N=350K). We compared LT-FH to genome-wide association without using family history (GWAS) and a previous proxy-based method for incorporating family history (GWAX). LT-FH was +63% (s.e. 6%) more powerful than GWAS and +36% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome-wide significant loci detected across all diseases (e.g. 690 independent loci for LT-FH vs. 423 for GWAS); the second best method was GWAX for lower-prevalence diseases and GWAS for higher-prevalence diseases, consistent with simulations. We also confirmed that LT-FH was well-calibrated (assessed via stratified LD score regression attenuation ratio), consistent with simulations. When using BOLT-LMM (instead of linear regression) to compute association statistics for all three methods (increasing the power of each method), LT-FH was +67% (s.e. 6%) more powerful than GWAS and +39% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX. In summary, LT-FH greatly increases association power in case-control association studies when family history of disease is available.

MAX-rank: a simple and robust genome-wide scan for case-control association studies

2008 ◽  
Vol 123 (6) ◽  
pp. 617-623 ◽  
Author(s):  
Qizhai Li ◽  
Kai Yu ◽  
Zhaohai Li ◽  
Gang Zheng
Keyword(s):  
Association Studies ◽  
Case Control ◽  
Genome Wide ◽  
Control Association ◽  
Genome Wide Scan

AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
Mathieu Emily
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Biological Interpretation ◽  
Limited Power ◽  
Control Association

AbstractAmong the large of number of statistical methods that have been proposed to identify gene-gene interactions in case-control genome-wide association studies (GWAS), gene-based methods have recently grown in popularity as they confer advantage in both statistical power and biological interpretation. All of the gene-based methods jointly model the distribution of single nucleotide polymorphisms (SNPs) sets prior to the statistical test, leading to a limited power to detect sums of SNP-SNP signals. In this paper, we instead propose a gene-based method that first performs SNP-SNP interaction tests before aggregating the obtained

Sign in / Sign up

Export Citation Format

Share Document