scholarly journals A Family-Based Rare Haplotype Association Method for Quantitative Traits

2018 ◽  
Vol 83 (4) ◽  
pp. 175-195 ◽  
Author(s):  
Ananda S. Datta ◽  
Shili Lin ◽  
Swati Biswas
Keyword(s):  
Quantitative Traits ◽  
Rare Haplotype ◽  
Haplotype Association ◽  
Association Method ◽  
Family Based

scholarly journals The impact of genotyping error on family-based analysis of quantitative traits

2001 ◽  
Vol 9 (2) ◽  
pp. 130-134 ◽  
Author(s):  
Gonçalo R Abecasis ◽  
Stacey S Cherny ◽  
Lon R Cardon
Keyword(s):  
Quantitative Traits ◽  
Genotyping Error ◽  
Family Based ◽  
The Impact

scholarly journals Leveraging population information in family-based rare variant association analyses of quantitative traits

2016 ◽  
Vol 41 (2) ◽  
pp. 98-107 ◽  
Author(s):  
Yu Jiang ◽  
Yunqi Ji ◽  
Alexander B. Sibley ◽  
Yi-Ju Li ◽  
Andrew S. Allen
Keyword(s):  
Rare Variant ◽  
Quantitative Traits ◽  
Rare Variant Association ◽  
Association Analyses ◽  
Population Information ◽  
Family Based

scholarly journals An Improved Version of Logistic Bayesian LASSO for Detecting Rare Haplotype-Environment Interactions with Application to Lung Cancer

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17290 ◽  
Author(s):  
Yuan Zhang ◽  
Swati Biswas
Keyword(s):  
Lung Cancer ◽  
Rare Variants ◽  
Computation Time ◽  
Rare Haplotype ◽  
Cancer Dataset ◽  
Bayesian Lasso ◽  
Haplotype Association ◽  
Gene Environment ◽  
Multiple Covariates ◽  
Logistic Bayesian Lasso

The importance of haplotype association and gene-environment interactions (GxE) in the context of rare variants has been underlined in voluminous literature. Recently, a software based on logistic Bayesian LASSO (LBL) was proposed for detecting GxE, where G is a rare (or common) haplotype variant (rHTV)-it is called LBL-GxE. However, it required relatively long computation time and could handle only one environmental covariate with two levels. Here we propose an improved version of LBL-GxE, which is not only computationally faster but can also handle multiple covariates, each with multiple levels. We also discuss details of the software, including input, output, and some options. We apply LBL-GxE to a lung cancer dataset and find a rare haplotype with protective effect for current smokers. Our results indicate that LBL-GxE, especially with the improvements proposed here, is a useful and computationally viable tool for investigating rare haplotype interactions.

scholarly journals On the association analysis of CNV data: a fast and robust family-based association method

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Meiling Liu ◽  
Sanghoon Moon ◽  
Longfei Wang ◽  
Sulgi Kim ◽  
Yeon-Jung Kim ◽  
...  
Keyword(s):  
Association Analysis ◽  
Association Method ◽  
Family Based

A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects

2004 ◽  
Vol 3 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Christoph Lange ◽  
Kristel van Steen ◽  
Toby Andrew ◽  
Helen Lyon ◽  
Dawn L DeMeo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Software Package ◽  
Quantitative Traits ◽  
Univariate Analysis ◽  
Principal Component ◽  
Genetic Effects ◽  
Association Test ◽  
Practical Relevance ◽  
Simulation Studies ◽  
Family Based

We propose a family-based association test, FBAT-PC, for studies with quantitative traits that are measured repeatedly. The traits may be influenced by partially or completely unknown factors that may vary for each measurement. Using generalized principal component analysis, FBAT-PC amplifies the genetic effects of each measurement by constructing an overall phenotype with maximal heritability. Analytically, and in the simulation studies, we compare FBAT-PC with standard methodology and assess both the heritability of the overall phenotype and the power of FBAT-PC. Compared to univariate analysis, FBAT-PC achieves power gains of up to 200%. Applications of FBAT-PC to an osteoporosis study and to an asthma study show the practical relevance of FBAT-PC. FBAT-PC has been implemented in the software package PBAT and is freely available at http://www.biostat.harvard.edu/~clange/default.htm.

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