Project 1: The Mouse Brain Library Project 2: Internet Microscopy (iScope) Project 3: Neurocartographer and Segmentation of the MBL Project 4: The Neurogenetics Tool Box
















Principal Investigator/Program Director Williams, Robert W.


QTLs for Retinal Ganglion Cells

Mapping CellSpecific QTLs. It is practical to map QTLs that affect individual neuronal populations. Williams and colleagues (1998) have done this type of fine-grained analysis for one of the more accessible populations of neurons in the CNSthe projection neurons of the retina, also known as retinal ganglion cells. One reason they chose this population is that it is possible to count retinal ganglion cells easily and precisely. Each cell has one and only one axon in the optic nerve, and a quantitative electron microscopic census of axons in a single cross-section of the nerve provides a reliable and unbiased estimate of total neuron number .

They began by estimating the size of this cell population in 510 individuals from each of 510 different inbred strains and extended the analysis to 20 common inbred strains . Variation among strains was substantial, and they decided to count ganglion cells in ~8 individuals from each of the 26 BXD strains and 12 BXH strains. Strain averages tended to fall into well-defined modes that corresponded to the parental strain averages of 55,000 (C57BL/6J) and 63,000 (DBA/2J). This striking non-normal distribution of phenotypes across the set of BXD RI strains suggested that two or three QTLs were modulating neuron number.

Using 26 strains of BXD mice that were then available, they were able to map the QTL that is primarily responsible for the bimodality of strain averages . They found an excellent correspondence between phenotypes and genotypes on chromosome 11 near the Tstap91a gene. The correlation between strains with high and low neuron number and alleles at Tstap91a was 0.69 . The genome-wide probability of getting a correlation this high by chance alone is less than 0.01.

These two examples of QTL mapping of quantitative neuroanatomical traits clearly demonstrate the utility of this approach in tackling issues of the genetic modulation of the CNS.





Gross Morphologic Measures