Block network mapping approach to quantitative trait locus analysis
| Autor: | Daniel Mario Gatti, Zeina Shreif, Vipul Periwal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
False discovery rate QTL mapping Bayes’ theorem Genotype Computer science Bayesian probability Quantitative Trait Loci Quantitative trait locus computer.software_genre Biochemistry Polymorphism Single Nucleotide 03 medical and health sciences Bayes' theorem Mice Structural Biology Inclusive composite interval mapping Animals Molecular Biology Block (data storage) Biological data Models Genetic Applied Mathematics Methodology Article Network mapping Chromosome Mapping Bayes Theorem Genomics Interval mapping Computer Science Applications 030104 developmental biology Data mining DNA microarray computer Algorithms |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| Popis: | Background Advances in experimental biology have enabled the collection of enormous troves of data on genomic variation in living organisms. The interpretation of this data to extract actionable information is one of the keys to developing novel therapeutic strategies to treat complex diseases. Network organization of biological data overcomes measurement noise in several biological contexts. Does a network approach, combining information about the linear organization of genomic markers with correlative information on these markers in a Bayesian formulation, lead to an analytic method with higher power for detecting quantitative trait loci? Results Block Network Mapping, combining Similarity Network Fusion (Wang et al., NM 11:333–337, 2014) with a Bayesian locus likelihood evaluation, leads to large improvements in area under the receiver operating characteristic and power over interval mapping with expectation maximization. The method has a monotonically decreasing false discovery rate as a function of effect size, unlike interval mapping. Conclusions Block Network Mapping provides an alternative data-driven approach to mapping quantitative trait loci that leverages correlations in the sampled genotypes. The evaluation methodology can be combined with existing approaches such as Interval Mapping. Python scripts are available at http://lbm.niddk.nih.gov/vipulp/. Genotype data is available at http://churchill-lab.jax.org/website/GattiDOQTL. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1351-8) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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