Meng Wang1, Shili Lin1. 1. Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
Abstract
MOTIVATION: In recent years, there has been an increasing interest in using common single-nucleotide polymorphisms (SNPs) amassed in genome-wide association studies to investigate rare haplotype effects on complex diseases. Evidence has suggested that rare haplotypes may tag rare causal single-nucleotide variants, making SNP-based rare haplotype analysis not only cost effective, but also more valuable for detecting causal variants. Although a number of methods for detecting rare haplotype association have been proposed in recent years, they are population based and thus susceptible to population stratification. RESULTS: We propose family-triad-based logistic Bayesian Lasso (famLBL) for estimating effects of haplotypes on complex diseases using SNP data. By choosing appropriate prior distribution, effect sizes of unassociated haplotypes can be shrunk toward zero, allowing for more precise estimation of associated haplotypes, especially those that are rare, thereby achieving greater detection power. We evaluate famLBL using simulation to gauge its type I error and power. Compared with its population counterpart, LBL, highlights famLBL's robustness property in the presence of population substructure. Further investigation by comparing famLBL with Family-Based Association Test (FBAT) reveals its advantage for detecting rare haplotype association. AVAILABILITY AND IMPLEMENTATION: famLBL is implemented as an R-package available at http://www.stat.osu.edu/∼statgen/SOFTWARE/LBL/.
MOTIVATION: In recent years, there has been an increasing interest in using common single-nucleotide polymorphisms (SNPs) amassed in genome-wide association studies to investigate rare haplotype effects on complex diseases. Evidence has suggested that rare haplotypes may tag rare causal single-nucleotide variants, making SNP-based rare haplotype analysis not only cost effective, but also more valuable for detecting causal variants. Although a number of methods for detecting rare haplotype association have been proposed in recent years, they are population based and thus susceptible to population stratification. RESULTS: We propose family-triad-based logistic Bayesian Lasso (famLBL) for estimating effects of haplotypes on complex diseases using SNP data. By choosing appropriate prior distribution, effect sizes of unassociated haplotypes can be shrunk toward zero, allowing for more precise estimation of associated haplotypes, especially those that are rare, thereby achieving greater detection power. We evaluate famLBL using simulation to gauge its type I error and power. Compared with its population counterpart, LBL, highlights famLBL's robustness property in the presence of population substructure. Further investigation by comparing famLBL with Family-Based Association Test (FBAT) reveals its advantage for detecting rare haplotype association. AVAILABILITY AND IMPLEMENTATION: famLBL is implemented as an R-package available at http://www.stat.osu.edu/∼statgen/SOFTWARE/LBL/.
Authors: Teri A Manolio; Francis S Collins; Nancy J Cox; David B Goldstein; Lucia A Hindorff; David J Hunter; Mark I McCarthy; Erin M Ramos; Lon R Cardon; Aravinda Chakravarti; Judy H Cho; Alan E Guttmacher; Augustine Kong; Leonid Kruglyak; Elaine Mardis; Charles N Rotimi; Montgomery Slatkin; David Valle; Alice S Whittemore; Michael Boehnke; Andrew G Clark; Evan E Eichler; Greg Gibson; Jonathan L Haines; Trudy F C Mackay; Steven A McCarroll; Peter M Visscher Journal: Nature Date: 2009-10-08 Impact factor: 49.962
Authors: Yanni Zeng; Pau Navarro; Masoud Shirali; David M Howard; Mark J Adams; Lynsey S Hall; Toni-Kim Clarke; Pippa A Thomson; Blair H Smith; Alison Murray; Sandosh Padmanabhan; Caroline Hayward; Thibaud Boutin; Donald J MacIntyre; Cathryn M Lewis; Naomi R Wray; Divya Mehta; Brenda W J H Penninx; Yuri Milaneschi; Bernhard T Baune; Tracy Air; Jouke-Jan Hottenga; Hamdi Mbarek; Enrique Castelao; Giorgio Pistis; Thomas G Schulze; Fabian Streit; Andreas J Forstner; Enda M Byrne; Nicholas G Martin; Gerome Breen; Bertram Müller-Myhsok; Susanne Lucae; Stefan Kloiber; Enrico Domenici; Ian J Deary; David J Porteous; Chris S Haley; Andrew M McIntosh Journal: Biol Psychiatry Date: 2016-12-16 Impact factor: 13.382