ProbABEL package for genome-wide association analysis of imputed data
By Yurii S. Aulchenko, Maksim V. Struchalin, Cornelia M. van Duijn. BMC Bioinformatics, 2010
446 citations recorded by the source corpus.
BackgroundOver the last few years, genome-wide association (GWA) studies became a tool of choice for the identification of loci associated with complex traits. Currently, imputed single nucleotide polymorphisms (SNP) data are frequently used in GWA analyzes. Correct analysis of imputed data calls for the implementation of specific methods which take genotype imputation uncertainty into account.ResultsWe developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations.ConclusionsProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/3077fd90fc23bf6475f40ff4f3744e3c003263a6
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In Genetics
- Evangelos Evangelou
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Citations in the corpus
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- Kalman-Based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning (2015, background): “Another example comes from genome-wide association studies in which multiple very large linear regression problems arise directly [38, 2].”
- Genome-Wide Association Study of Retinopathy in Individuals without Diabetes (2013, unknown): “ru/̃ yurii/ABEL/) [33] in AGES, ARIC, BMES and RS.”