ArticleBMC bioinformatics2011
CAPL: an efficient association software package using family and case-control data and accounting for population stratification.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- PBX-WNT-P63-IRF6 pathway in nonsyndromic cleft lip and palate.Birth defects research · 2020Article
- Haplotype-specific modulation of a SOX10/CREB response element at the Charcot-Marie-Tooth disease type 4C locus SH3TC2.Human molecular genetics · 2014Article
- Further evidence suggesting a role for variation in ARHGAP29 variants in nonsyndromic cleft lip/palate.Birth defects research. Part A, Clinical and molecular teratology · 2014Article
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Authors and funding
3 authors.
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Abstract
backgroundWith many genome-wide association study (GWAS) datasets available, it is critical that we have statistical tools that are both flexible to accommodate different study designs and fast. We recently proposed the combined APL (CAPL) method, which can use family and case-control datasets and can account for population stratification in the data. Because computationally intensive algorithms are used in CAPL, implementing CAPL with efficient parallel algorithms is essential.
resultsWe used a hybrid of open message passing interface (open MPI) and POSIX threads to parallelize CAPL, which enable the program to operate in a cluster environment. We used simulations to demonstrate that the parallel implementation of CAPL can analyze a large GWAS dataset in a reasonable time frame when a parallel computing resource is available.
conclusionsAs many GWAS datasets based on both family and case-control designs are available, a flexible and efficient tool such as CAPL will be very helpful to combine the datasets to greatly increase statistical power and finish the analysis in a reasonable time frame.
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