This is the main output type: a so-called full genome interval map.
	The X-axis represents all 19 autosomes and the X chromosome as if they were laid end to end with short gaps between the telomere of one chromosome and the centromere of the next chromosome (mouse chromsomes only have a single long arm and the centromere represents the origin of each chromosome for numerical purpose: 0 centimorgans and almost 0 megabases). The blue labels along the bottom of the figure list a subset of markers that were used in mapping. We used 753 markers to perform the mapping but here we just list five markers per chromosome.
	The thick blue wavy line running across chromsomes summarizes the strength of association between variation in the phenotype (App expression differences) and the two genotypes of 753 markers and the intervals between markers (hence, interval mapping).  The height of the wave (blue Y-axis to the left) provides the likelihood ratio statistic (LRS). Divide by 4.61 to convert these values to LOD scores.  Or you can read them as a chi-square-like statistic.
	The red line and the red axis to the far right provides an estimate of the effect  that a QTL has on expression of App (this estimate of the addtive effect tends to be an overestimate). If the red line is below the X-axis then this means that the allele inherited from C57BL/6J (B6 or B) at a particular marker is associated with higher values. If the red line is above the X-axis then the DBA/2J allele (D2 or D) is associated with higher traits. Multiply the additive effect size by 2 to estimate the difference between the set of strains that have the B/B genotype and the D/D genotype at a specific marker. For example, on Chr 2 the red line  peaks at a value  of about 0.25. That means that this region of chromosome 2 is responsible for a 0.5 unit expression difference between B/B strains and the D/D strains. Since the units are log base 2, this is 2^0.5, or about a 41% difference in expression with the D/D group being high.
	The yellow histogram bars: These summarize the results of a whole-genome bootstrap of the trait that is performed 1000 times. What is a bootstrap? A bootstrap provides you a metho of evaluating whether results are robust. If we drop out one strain, do we still get the same results? When mapping quantitative traits, each strain normally gets one equally weighted vote. But inthe bootstrap procedure, we give each strain a random weighting factor of between 0 and 1.  We then remap the trait and find THE SINGLE BEST LRS VALUE per bootstrap. We do this 1000 times. In this example, most bootstrap results cluster on Chr 2 under the LRS peak. That is somewhat reassuring. But notice that a substantial number of bootstrap results prefer Chr 7 or Chr 18.
	The horizontal dashed lines at 9.6 and 15.9. These lines are the LRS values associated with the suggestive and significant false positive rates for genome-wide scans established by permuations of phenotypes across genotypes. We shuffle randomly 1000 times and obtain a distribution of peak LRS scores to generate a null distribution. Five percent of the time, one of these permuted data sets will have a peak LRS higher than 15.9. We call that level the 0.05 significance threshold for a whole genome scan. The p = 0.67 point is the the suggestive level, and corresponds to the green dashed line.  These thresholds are conservative for transcripts that have expression variation that is highly heritable. The putative or suggestive QTL on Chr 2 is probably more than just suggestive.
	One other point: the mapping procedure we use is computationally very fast, but it is relatively simple. We are not looking for gene-gene interactions and we are not fitting multiple QTLs in combinations.  Consider this QTL analysis a first pass that will highlight hot spots and warm spots that are worth following up on using more sophisticated models.
	CLICKABLE REGIONS:
	1. If you click on the Chromosome number then you will generate a new map just for that chromosome.
	2. If you click on the body of the map, say on the blue line, then you will generate a view  on a 10 Mb window of that part of the genome from the UCSC Genome Browser web site.
	3. If you click on a marker symbol, then you will generate a new Trait data and editing window with the genotypes loaded into the window just like any other trait. More on this later.
	NOTE: you can drag these maps off of the browser window and onto your desktop. The will be saved as PNG or PDF files. You can import them into Photoshop or other programs.