title: Block network mapping approach to quantitative trait locus analysis.
abstract: 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 .
authors.affiliation: Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Zeina Z
authors.initials: ZZ
authors.lastname: Shreif
authors.affiliation: The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Gatti
authors.affiliation: Laboratory of Biological Modeling, NIDDK, National Institutes of Health, Bethesda, MD 20892, USA. vipulp@mail.nih.gov.
authors.forename: Vipul
authors.initials: V
authors.lastname: Periwal
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002874:Chromosome Mapping; D023281:Genomics; D005838:Genotype; D051379:Mice / Q000235:genetics*; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci
keywords: Bayes’ theorem; Interval mapping; QTL mapping