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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 .

Mouse genetics is a powerful approach for discovering genes and other genome features influencing human pain sensitivity. Genetic mapping studies have historically been limited by low mapping resolution of conventional mouse crosses, resulting in pain-related quantitative trait loci (QTL) spanning several megabases and containing hundreds of candidate genes. The recently developed Diversity Outbred (DO) population is derived from the same eight inbred founder strains as the Collaborative Cross, including three wild-derived strains. DO mice offer increased genetic heterozygosity and allelic diversity compared to crosses involving standard mouse strains. The high rate of recombinatorial precision afforded by DO mice makes them an ideal resource for high-resolution genetic mapping, allowing the circumvention of costly fine-mapping studies. We utilized a cohort of ~300 DO mice to map a 3.8 Mbp QTL on chromosome 8 associated with acute thermal pain sensitivity, which we have tentatively named Tpnr6. We used haplotype block partitioning to narrow Tpnr6 to a width of ~230 Kbp, reducing the number of putative candidate genes from 44 to 3. The plausibility of each candidate gene's role in pain response was assessed using an integrative bioinformatics approach, combining data related to protein domain, biological annotation, gene expression pattern, and protein functional interaction. Our results reveal a novel, putative role for the protein-coding gene, Hydin, in thermal pain response, possibly through the gene's role in ciliary motility in the choroid plexus-cerebrospinal fluid system of the brain. Real-time quantitative-PCR analysis showed no expression differences in Hydin transcript levels between pain-sensitive and pain-resistant mice, suggesting that Hydin may influence hot-plate behavior through other biological mechanisms.

The Diversity Outbred (DO) population is a heterogeneous stock derived from the same eight founder strains as the Collaborative Cross (CC) inbred strains. Genetically heterogeneous DO mice display a broad range of phenotypes. Natural levels of heterozygosity provide genetic buffering and, as a result, DO mice are robust and breed well. Genetic mapping analysis in the DO presents new challenges and opportunities. Specialized algorithms are required to reconstruct haplotypes from high-density SNP array data. The eight founder haplotypes can be combined into 36 possible diplotypes, which must be accommodated in QTL mapping analysis. Population structure of the DO must be taken into account here. Estimated allele effects of eight founder haplotypes provide information that is not available in two-parent crosses and can dramatically reduce the number of candidate loci. Allele effects can also distinguish chance colocation of QTL from pleiotropy, which provides a basis for establishing causality in expression QTL studies. We recommended sample sizes of 200-800 mice for QTL mapping studies, larger than for traditional crosses. The CC inbred strains provide a resource for independent validation of DO mapping results. Genetic heterogeneity of the DO can provide a powerful advantage in our ability to generalize conclusions to other genetically diverse populations. Genetic diversity can also help to avoid the pitfall of identifying an idiosyncratic reaction that occurs only in a limited genetic context. Informatics tools and data resources associated with the CC, the DO, and their founder strains are developing rapidly. We anticipate a flood of new results to follow as our community begins to adopt and utilize these new genetic resource populations.

The JAX Diversity Outbred population is a new mouse resource derived from partially inbred Collaborative Cross strains and maintained by randomized outcrossing. As such, it segregates the same allelic variants as the Collaborative Cross but embeds these in a distinct population architecture in which each animal has a high degree of heterozygosity and carries a unique combination of alleles. Phenotypic diversity is striking and often divergent from phenotypes seen in the founder strains of the Collaborative Cross. Allele frequencies and recombination density in early generations of Diversity Outbred mice are consistent with expectations based on simulations of the mating design. We describe analytical methods for genetic mapping using this resource and demonstrate the power and high mapping resolution achieved with this population by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes. Analysis of the estimated allele effects in conjunction with complete genome sequence data of the founder strains reduced the pool of candidate polymorphisms to seven SNPs, five of which are located in an intergenic region upstream of the Foxo1 gene.

Gene expression quantitative trait locus (eQTL) mapping has become a powerful tool in systems biology. While many authors have made important discoveries using this approach, one persistent challenge in eQTL studies is the selection of loci and genes that should receive further biological investigation. In this study we compared eQTL generated from gene expression profiling in the livers of two panels of mouse strains: 41 BXD recombinant inbred and 36 Mouse Diversity Panel (MDP) strains. Cis-eQTL, loci in which the transcript and its maximum QTL are colocated, have been shown to be more reproducible than trans-eQTL, which are not colocated with the transcript. We observed that between 9.9 and 12.1% of cis-eQTL and between 2.0 and 12.6% of trans-eQTL replicated between the two panels depending on the degree of statistical stringency. Notably, a significant eQTL hotspot on distal chromosome 12 observed in the BXD panel was reproduced in the MDP. Furthermore, the shorter linkage disequilibrium in the MDP strains allowed us to considerably narrow the locus and limit the number of candidate genes to a cluster of Serpin genes, which code for extracellular proteases. We conclude that this strategy has some utility in increasing confidence and resolution in eQTL mapping studies; however, due to the high false-positive rate in the MDP, eQTL mapping in inbred strains is best carried out in combination with an eQTL linkage study.