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Note to the Reader This is a revised edition of a paper published in Seminars in Cell and Developmental Biology. The definitive original print version is available from Academic Press and on-line at the Idealibrary at
New figures, text, and links have been incorporated into the revision. Revised HTML ( copyright ©1999 by Robert W. Williams Seminars in Cell & Developmental Biology (1998) 9:249–255.

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Genetic Dissection of Retinal Development

Robert W. Williams, Richelle C. Strom, Guomin Zhou, and Yan Zhen
Center for Neuroscience and Department of Anatomy and Neurobiology
School of Medicine, University of Tennessee, 855 Monroe Avenune, Memphis TN 38163 USA

Genetic Analysis of Retinal Ganglion Cells
Genetic Analysis of Horizontal Cells
Data on Horizontal Cell Density and Number for 7 Strains (tab-delimited text)
Genetic Dissection of Eye Size

Related sites of interest:
A Tutorial on Mapping QTLs affecting CNS Structure
A Review of Mapping Genes that Modulate Brain Development
The Retinal Information Network



Retinal development depends on complex interactions between products of thousands of genes and numerous cellular and environmental factors. We are using novel quantitative genetic methods to map and characterize genes that are responsible for the pervasive quantitative differences in the architecture of the eye and the retina. These genes, known as quantitative trait loci (QTLs), may also determine susceptibility to common eye diseases. To map QTLs that generate variation among normal individuals we have analyzed several traits in a wide variety of mice, including standard inbred strains, recombinant inbred strains, wild mice, F1 hybrids, and intercross progeny. Here we review this approach and give three specific examples of how genes with well-defined functions in retinal development are being mapped and characterized.



The genetic complexity of the retina

Retinal cDNA libraries contain more than 15,000 transcripts generated from as many as 10,000 genes. Half of these genes may have common roles in cellular metabolism, but the other half contribute more specifically to retinal development and function. Understanding how such large populations of genes interact with each other and with many exogenous factors to generate and maintain the retina is a major intellectual challenge, but there are reasons for optimism. Techniques are advancing rapidly and progress is being made in tracing the intertwined molecular and cellular pathways of eye development. Within twenty years we can anticipate having sophisticated databases of gene expression in individual retinal cell types. We may, for example, know when differences in gene expression first distinguish ON- and OFF-bipolar cells; we may know what genes contribute to the remarkable diversity of amacrine cell phenotypes, and we may know what genes help differentiate ganglion cells into populations with crossed and uncrossed projections.


Genetics of natural variation

This leaves an important area of research untouched—namely, which of these thousands of genes generate natural variation in the retina. Which genes contribute to the three-fold difference in cone density in the foveal pit of humans (Curcio et al. 1987)? Which genes predispose some of us to myopia or glaucoma? Which genes help generate variation in ratios of rods and cones (Wikler et al., 1990)? To answer these types of questions we need to know which genes are normally variable, and we need to know how allelic variants produce significant differences in the development and organization of the eye. This type of information is not only critical in understanding sources of variation within a species, but it is also critical in understanding the amazing variety of eyes that natural selection has produced by relatively subtle genetic changes (Walls, 1942; Wikler and Rakic, 1990; Williams et al., 1993).


Common gene polymorphisms and rare mutations

This article describes and illustrates a strategy that we are using to answer these types of questions (Williams et al., 1996, 1998). Our aim is to find and characterize an important set of genes responsible for normal differences in retinal structure among mice. Almost all heritable variation is produced by polymorphic genes—genes that have two or more common alleles. About 10% to 20% of genes are polymorphic in vertebrate populations (Nei, 1987; Levinton, 1988). This implies that 1000 polymorphic genes may contribute to variation in the eye and retina within most species. Nothing is now known about these genes. Our focus on normal variants complements the analysis of rare mutations that disrupt retinal development. It will be important to establish if any of the long list of mutated genes that perturb retinal development also have normal allelic variants with more tempered effects.


Mapping QTLs that generate variation of retinal architecture

Until recently, it has not been practical to dissect complex traits controlled by large numbers of genes in vertebrates (Lander and Schork, 1994). Genome maps were sparse and genotyping was hard work. However, we now have high density genome maps, as well as rapid and sensitive methods that make it practical to map the quantitative trait loci (QTLs) that generate normal variation in heritable traits. The polymerase chain reaction (PCR) combined with gel electrophoresis is the current choice for high-throughput genotyping, but chip-based methods that promise a huge improvement in efficiency will dominate within a decade (Shoemaker et al., 1996). A forward genetic approach (from traits to genes) can now be used to map functional categories of genes from among the thousands that are involved in retinal development. For example, it is now possible to find specific QTLs that modulate ratios of rods and cones or that modulate numbers of early- and late-generated retinal cell types. We do not have to wait for a spontaneous mutation or a knockout mouse with the right effects. We have started this type of analysis by studying several important and easily measured traits: eye weight, total retinal surface area, the density of horizontal cells, and the total number of retinal ganglion cells (Williams et al., 1996, 1998; Strom and Williams, 1998). We have begun with these traits because of their functional significance and because they are practical to measure in large numbers of animals.


The Essence of QTL mapping

Mapping a gene involves finding a pattern of allelic differences that correspond with phenotypic differences in a set of animals (Tanksley, 1993; Lander and Schork, 1994; Williams, 1998). If every gene had two alleles (the alpha gene had alleles A+ and A–, the beta gene had alleles B+ and B–, etc.), and if all of these genes assorted independently during the production of gametes (one gene per chromosome), then all we would need to do to map a set of QTLs would be to discover which genes had alleles that correlated much better than expected by chance with animals with high, low, and intermediate phenotypes. For example, if in a pool of 400 F2 animals, 100 progeny with a B–B– genotype had small eyes, 200 progeny with the B–B+ genotype had intermediate-sized eyes, and 100 progeny with the B+B+ genotype had large eyes we would be well justified in considering the beta gene a very important QTL. The disadvantage of this one-gene-per-chromosome scenario is that we would have to test all genes individually. The analysis would involve an enormous amount of genotyping.


Linkage and non-independent assortment

Genes are, of course, actually strung together on chromosomes, and these linked genes tend to stay together during meiosis and are inherited in groups (also known as haplotypes). The closer together a set of polymorphic genes are on the same chromosome, the lower the probability that alleles of neighboring genes will recombine during meiosis. A consequence of linkage is that even loci that do not actually affect a trait will often have a strong statistical association with that trait, provided that they are linked closely enough to the responsible gene. If the alpha gene in our example above is linked to the beta gene, then the distribution of A+ and A– alleles at the alpha locus will be nearly as well correlated with eye size as it is with the beta gene that actually modulates eye size. This linkage is a help in mapping QTLs, because to discover the approximate location of a QTL we only need to test a small number of easily typed loci on each chromosome. Using interval mapping techniques developed in the last ten years (Tanksley, 1993; Lander and Schork, 1994), marker loci that are close to QTLs (within 10–20 centimorgan) will often reveal strong correlations with variation in the phenotype. In a search for QTLs across the entire genome, it is usually adequate to genotype 50 to 100 well-spaced loci in several hundred progeny.

The approach used to map QTLs has five key steps:

1. Estimate the relative importance of environmental and genetic factors in controlling variation in a trait. Before attempting to map genes we need to be certain that variation is, in fact, heritable. If variation between inbred strains is much greater than the non-genetic variation within inbred strains, then heritability will be high and it should be practical to map one of more QTLs. If a trait has a low heritability then it may still be possible to map QTLs, but doing so may require much work.

2. Estimate the minimum number of genes. To estimate the genetic complexity of a trait we use a simple method in which the variances of inbred strains, F1, and F2 progeny are compared (Lande, 1981). This step is not essential, but if the number of genetic factors affecting a trait is less than five we can be optimistic about the prospects of mapping one or two QTLs. In contrast, if a large number of QTLs contribute to the variance, then locating QTLs may be difficult.

3. Linkage analysis of QTLs using recombinant inbred strains and intercross progeny. There are several tactics that can be used to find the approximate chromosomal location of QTLs. Each relies on different types of progeny (recombinant inbred strains, F2 intercross, backcross, an advanced intercross progeny, recombinant congenic strains), but the key is always to find a strong association between genotypes and phenotypes for a particular marker locus close to the presumed QTL. These tactics are compared in Williams (1998).

4. High resolution mapping and cloning of QTLs. Fine-mapping QTLs to within 1 cM is a developing art. Even a few years ago, the prospects of routinely cloning QTLs seemed remote. However, several schemes have been devised to successively narrow the interval that must be examined to clone the right gene or to test a hopefully small set of candidate genes (Darvasi, 1997). An analysis of knock-out mice is one simple approach to test candidate genes. The advent of high resolution transcript maps will further simplify isolating and testing viable candidates in a given chromosomal region.

5. Functional analysis of allelic variants at the QTL. Once a strong candidate gene has been identified, the focus of research can shift back to studying the molecular and cellular biology of the candidate gene using the same wide variety of techniques now applied to the analysis of a newly cloned mutation affecting retinal development.



Genetic dissection of the retinal ganglion cell population

This work started with the discovery of an astonishingly high rate of ganglion cell loss in fetal cats—5 out of 6 retinal ganglion cells die during development (Williams et al., 1986). We found it difficult to make sense of this massive loss: the level is significantly higher than inother species, including humans and rhesus monkeys, and neither error correction nor lack of trophic support seems to justify the cell decimation. An analysis of the retina of a subspecies of wildcat (Felis silvestris tartessia) that is ancestral to domestic cats, raised the possibility that ganglion cell loss might be caused by genetic changes associated with a rapid reduction in brain and body size in the lineage that has led to the domestic cat (Williams et al., 1993). Perhaps genetic mechanisms associated with the two-fold reduction in body size that has occurred over the last 20,000 years in the cat lineage, has been assoicated with genetic changes that have increased cell death late in development. The genetic of these types of questions is currently only practical using mice, and consequently, over the past several years research has shifted to this species. We started by counting ganglion cells in a small set of inbred strains (Rice et al., 1995). Variation in ganglion cell numbers between strains was high and we were sufficiently intrigued by the results to begin a quantitative genetic analysis (Williams et al., 1996). (The current database on the ganglion cell population—available at—consists of a sample of over 856 animals, and includes information on sex, age, body weight, brain weight, eye weight, and retinal area.)

We find that ganglion cell number is highly variable in mice, ranging from 40,000 to 80,000 (Wiliams et al., 1996). Heritability is 70% to 90%, and this high estimate justified moving to step 2—the analysis of the minimum number of genes controlling normal variation in ganglion cell number. The result of a cross between CAST/Ei and BALB/cJ, strains with low and high cell number, respectively, are illustrated in Figure 1. Variance in the parental strains, and in isogenic F1 hybrids is significantly lower than that among F2 progeny. The segregation of high and low alleles at a single QTL could in principle account for almost all of the genetic variance. This encouraging result was corroborated by an analysis of inbred strain averages. These strain averages fell neatly into two groups—one centered close to 55,000, the other centered close to 64,000. This strongly suggests the presence of a single major QTL with high and low alleles.

Figure 1

Figure 1. Estimating the minimum number of genes controlling retinal ganglion cell number. To the far left are two boxed sets of data points for individual mice belonging to the two parental strains, BALB/cJ and CAST/Ei. These animals were mated to produce the F1 generation. F1 values are higher than the midpoint between the parents, indicative of gene dominance or maternal effect. The F2s are shown to the right. Note that the spread of values in the F2 is greater than in the other groups. This increase in variance is due to the independent segregation of alleles at QTLs affecting ganglion cell number. The equation at the bottom of the figure was used to estimate the minimum gene number, where delta P is the difference in the mean parental values, and V is the variance in the F1 and F2 generations. For this particular cross the value is less than 1, indicating that a single gene could account for all of the variance increase in the F2 generation.


With such favorable indicators, we began counting ganglion cells in BXD and BXH recombinant inbred (RI) strains (Williams et al., 1998). RI strains are most often used to map Mendelian traits, and over 2000 loci have been mapped using these particular strains. But RI strains are also an excellent resource for mapping QTLs. One significant advantage is that environmental variance can be reduced substantially by phenotyping many mice that have the same genotype. We typed an average of six mice per strain to get an accurate estimate of the average ganglion cell population associated with each genotype.

A second advantage is that RI strains are generated by a process that results in a four-fold expansion of the genetic map. A result is that QTLs that have prominent effects can be mapped with remarkably high spatial precision. Finally, these strains are fully inbred, and the absence of heterozygotes increases the genetic variance twofold compared to a set of intercross or backcross progeny.

Using this RI approach we successfully map a QTL named neuron number control 1 (Nnc1) to a 3 cM interval between Hoxb and Krt1 on chromosome 11. There are three strong candidate genes for this QTL—Erbb2, Rara, and Thra. Each encodes a receptor expressed in retina during development. Furthermore, changing ligand concentrations of these receptors affects the proliferation or survival of retinal cells. For example, an increase in triiodothyronine, the ligand of the Thra receptor, triggers the production of a new set of ganglion cells with ipsilateral projections in Xenopus (Hoskins, 1985). We are now testing the viability of these candidate genes by counting ganglion cells in mice in which these one or both alleles have been inactivated by homologous recombination. The QTL responsible for the large strain differences controls proliferation rather than cell death (Strom and Williams, 1998).



2. Genetic dissection of horizontal cell density


Horizontal cells have a critical role in shaping the surround responses of photoreceptors and bipolar cells (Sterling, 1998). We have used an antibody directed against the 28 kDa calcium-binding molecule, calbindin, to label essentially the entire population of horizontal cells in the mouse (Figure 2).

Figure 2
Figure 2. A two-fold difference in horizontal cell density in two strains of mice. Calbindin-positive horizontal cells in strains C57BL/6J, and A/J. Both micrographs were photographed at the same magnification. Calibration bar is 100 µm.



There are very significant differences in densities of these cells among the strains listed in Table 1. The greatest difference is between C57BL/6J and A/J. As was true of retinal ganglion cells, heritability is very high. We do not yet know how many genes influence the number and density of calbindin-positive horizontal cells. However, the intermediate density and the low variation within F1 hybrids from a cross between A/J and C57BL/6J (see B6AF1/J in table 1) provides a strong incentive to examine these cells in the set of 31 RI strains generated by crossing A/J and C57BL/6J. It should be practical to map major QTLs controlling variation in horizontal cell density and number. From a functional perspective it is interesting to note that the ratio of ganglion cells to horizontal cells varies from 3.2 to 6.7.


 Table 1. Two-fold variation in horizontal cell densities

Strain Density ±SE* Range n cases n retinas


A/J 561 ± 8.09 535–592 4 7
B6AF1/J 754 ± 26.8 641–878 5 9
C57BL/6J 1151 ± 17.5 1087–1203 6 8
AKR/J 778 ± 34.8 719–856 2 4
C57BKS/J 973 ± 60.0 804–1092 3 5
DBA/2J 1029 ± 21.3 994–1067 3 4
M. caroli 711 ± 25.1 675–746 2 3

  *Calbindin-positive horizontal cell density per 1 mm2.
  Multiple sample areas of 0.24 to 0.36 mm2 were counted in each retina.



3. Genetic dissection of eye weight


The size of the eye is a important determinant of maximum light gathering ability and of maximum acuity. Eye size is also a clinical important trait because myopia—by far the most pervasive eye abnormality in humans—is usually caused by excessive growth of the eye relative to the refractive power of the cornea and lens. For these reasons we are interested in determining whether there are QTLs that control the overall growth of the eye. As above, the first step is to determine how variable eye size is within and between strains of mice. Eye weight is easy to measure and can be obtained rapidly for large numbers of animals. Eye weight in the set of 11 strains listed in Table 2 varies from 14.8 mg in SJL/J to 18.9 mg in CE/J. Three of the strains with small eye weight are homozygous for the rd mutation in the beta phosphodiesterase locus, and this association may be more than just chance. Genetic factors in a broad sense account for approximately 30% of the differences among cases. This is a high enough value to justify an attempt to estimate numbers of genes affecting eye weight and then, if possible, to locate and characterize underlying QTLs.



 Table 2. Eye weight, retinal area, and retinal ganglion cell number

Strain* Eye Weight ±SE CV% N Area ±SE RGCs±SE x1000

SJL/J-rd/rd 14.8 ±0.2 3.4 7 15.3 ±0.8 52.5 ±1.8
PL/J-rd/rd 15.2 ±0.3 4.2 5 18.0 ±0.8 56.0 ±1.3
C3H/HeJ-rd/rd 16.0 ±0.2 4.3 10 17.8 ±0.4 67.0 ±1.7
A/J 16.5 ±0.5 8.3 8 16.5 ±0.7 50.6 ±1.3
CBA/CaJ 16.6 ±0.3 4.5 6 18.6 ±0.5 56.0 ±1.2
C57BL/6J 17.6 ±0.2 6.1 35 18.5 ±0.3 54.6 ±0.9
AKR/J 18.2 ±0.3 3.8 6 18.6 ±0.0 62.8 ±0.9
BXD27 18.3 ±0.4 7.7 15 18.9 ±0.4 50.8 ±1.1
BXD5 18.4 ±0.4 8.2 17 19.0 ±0.1 75.5 ±1.3
DBA/2J 18.8 ±0.4 9.2 16 20.1 ±0.6 65.4 ±1.2
CE/J 18.9 ±0.4 5.7 7 18.2 ±0.1 63.6 ±2.5

* Three of these strains are homozygous rd mutants at the phosphodiesterase locus.

SE = Standard error of the mean, CV% is the coefficient of variation. Eye weight is measured in mg. Retinal area is measured in mm2. RGC is the population of retinal ganglion cells. Comparisons of weights of unfixed right eyes and fixed left eyes reveal approximatelya 1 mg weight loss (6%) following fixation. All of the eye weights listed below are fixed weights. A regression analysis was used to neutralize a significant age-related increase in eye weight. All weights are normalized to 75 days. The correlation coefficient between eye weight and retinal area is 0.75, that between weight and cell number is 0.44, and that between area and number is 0.49.


Numbers of QTLs affecting eye weight. If eye weight is controlled by a large number of QTLs, then individual QTLs may not have a large enough effect to be mapped. Our estimate of gene number is based on a comparison of the variance in inbred strains and F1 and F2 progeny. In contrast to ganglion cell number, a minimum of 10 QTLs appear to be responsible for variation in eye weight. This estimate is two times higher than a similar estimate generated by Lande (1981) using data from Wilkens' classic genetic dissection of eye size in blind cavefish (1971). If each of the 10 or more QTLs accounted for an equal fraction of the genetic variance, then individual QTLs would have small effects and would be hard to map.

The 1.2 mg difference in Table 2 between C57BL/6J and DBA/2J is interesting because these strains were used to generate the set of BXD RI strains with which we succeeded in mapping the Nnc1 locus. To map eye weight QTLs we compared the distribution of eye weights in BXD strains with the distribution of alleles at more than 500 previously mapped gene loci. A major QTL, Eye1, was mapped to proximal chromosome 5 (Zhou and Williams, 1997, 1998). This locus does not map near the beta phosphodiesterase locus or any of the other 113 mutations that are known to affect eye and retinal development in the mouse. To refine the position of Eye1 we are now generating an advanced intercross (Darvasi, 1997) that should enable us to map this locus to within 1–3 cM. We hope to be in a position to test candidate genes that normally regulate growth of the eye over the next few years.



The analysis of genes controlling normal variation in vertebrates is at an early stage. Our experience suggests that it will be practical to map QTLs that have comparatively large phenotypic effects on most heritable traits using RI strains and F2 intercrosses. As methods are refined we should be able to map QTLs that have more subtle effects on retinal and ocular traits. As increased numbers of QTLs are mapped, it is likely that the same QTL will often be discovered repeatedly for traits that were initially thought to be independent. Identifying QTLs with pleiotropic effects has the potential of exposing common regulatory and genetic mechanisms in different tissues. A good example is the surprising common effects that cyclin D1 have on breast and retinal development (Sicinski et al., 1995).

Most developmental biologists are interested in understanding molecular and cellular pathways that lead to the proliferation and differentiation of cells and tissues. Their aim is to understood representative organisms in ultimate detail. The molecular conservation of metazoan development has resulted in a highly productive cross-fertilization between research on nematodes, flies, fish, frogs, birds, mice, and humans. But the appreciation of deep-rooted molecular conservation has led to an unfortunate neglect of the genetic basis of the remarkable variation within and among species. This variation is primarily quantitative and is the untrampled research path that we have chosen to explore. At one level of analysis, the QTLs that we are isolating and characterizing are responsible for only minor variation in retinal development. They do not produce dramatic mutations that garner intense attention. But at another level of analysis, it is precisely these variants that over many generations of selection have produced a variety of eyes that are well adapted for vision in vastly different environments.


Acknowledgment. We thank Dr. Anand Swaroop for helpful discussion. This work was supported by NEI RO1EY0662 to RWW. Research on the Nnc1 locus is supported by NS35485 to RWW.



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