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Note to the Reader
A Dissertation Presented for The Graduate Studies Council The University of
Tennessee, Memphis In Partial Fulfillment Of the requirements for the Degree
Doctor of Philosophy
From the University of Tennessee By Richelle Cutler Strom December 1999
Copyright ©1999 Richelle Cutler Strom All rights reserved
Genetic Analysis of Variation in Neuron Number
Richelle Cutler
Strom
ACKNOWLEDGMENTS
I would like to thank my mentor, Dr. Robert Williams, for his guidance
and patience. I would like to thank Dr. Dan Goldowitz for his teaching early
in my graduate training (in the mouse room and laboratory), and also later
for his assistance as a committee member. I also thank my committee members,
Drs. Andrea Elberger, Karen Hasty, and Michael Dockter for their assistance.
I would like to acknowledge the people who have assisted me in the technical
aspects of my dissertation research. I especially thank Mrs. Xiyun Peng for
her expert technical assistance with genotyping. I thank Kathy Troughton for
her assistance in sectioning the optic nerves and for teaching me electron
microscopy. I also like to thank Richard Cushing for his help with the
histology. I would like to express my gratitude to my UT friends whom have
made graduate school a recreative experience. These friends are Drs. Dennis
Rice, Kristin Hamre, Mike Fowler, Guomin Zhou and Toya Kimble, Qing Tang,
and Trisha Jensen.
I would like to thank the members of my family, especially my mother and
father, who encouraged me to pursue this path from the start. Finally, I
would like to express my sincere appreciation to my husband Jimmy for his
support and understanding. He gave me the strength to "just do it".
Abstract
There are large differences in neuron number both within and between
species. This variation in neuron number results from both non-genetic and
genetic factors. Non-genetic factors, such as litter size and parity, and
genetic cofactors, such as sex, age, and body weight, are known to generate
variation in neuron number. However, the cumulative variance in neuron
number that can be explained by the summation of these factors is unknown.
Genetic variation has been shown to explain a substantial portion of the
variation in neuron number. However, the identity of the genetic factors and
the manner in which they influence neuron number are not known. In this
dissertation, I have analyzed the components of environmental and genetic
variation contributing to variation in neuron number among inbred strains of
mice. I have focused on variation in neuron number on a large scale, by
using the surrogate measure of whole brain weight, and variation within a
distinct neuron population, retinal ganglion cells.
Brain weight ranges from 403 mg to 495 mg among standard inbred strains
of mice. I have assessed the relative importance of environmental and
genetic factors in the variation of brain weight in 27 standard inbred
strains, two sets of recombinant inbred strains, and four F2
intercrosses. I first estimated the portion of variance in brain weight due
to sex, age, body weight, litter size, and parity by regression analysis.
Sex, age, parity, and body weight account for approximately half of the
variance in brain weight, with body weight being the most important
variable. However, the remaining variance in brain weight is substantial.
Significant genetic variation was evident from the significantly lower
variance in brain weight within strains compared to across strains, or
within heterogeneous mice. Heritability of brain weight in the inbred
strains is 0.50 . The broad platykurtic and nearly bimodal probability
density distributions of two intercrosses indicate that genes with major
effects on brain weight are segregating within these crosses. In addition,
my estimates of the minimum number of genes modulating brain weight within
the crosses ranged from one to six. The high heritability and low effective
gene number indicate that it should be possible to map some of the genes
modulating brain weight between strains. Genes that produce variation in a
quantitative trait, such as brain weight, are called quantitative trait loci
(QTLs).
I mapped QTLs responsible for variation in brain weight using two
recombinant inbred sets (BXD and AXB/BXA) and one F2
intercross (ABXDF2, n = 517. Using linkage
analysis, with composite interval mapping, I detected four significant QTLs
affecting brain weight on Chrs 7, 11, and 14. The brain weight QTLs have
been named Brain size control 1, 2, 3, and 4 (Bsc1, 2, 3,
& 4). Bsc1 maps to proximal Chr 11 at 12 cM and has a LOD
score of 8.4. Bsc2 maps to distal Chr 7 at 65.2 cM and has a LOD
score of 6.7. Bsc3and Bsc4 map to Chr 14 at 25 cM and 59.1 cM
and have LOD scores of 6.8 and 5.9, respectively. Secondary brain weight
QTLs were mapped to Chrs 1, 5, 8, 11, 14, 18, and X. In the ABXD5F2
cross, three QTLs, Bsc3 and Bsc4, and a secondary QTL on
Chr 18, were each estimated to explain between 4% to 6% of the variance in
brain weight. These three QTLs account for 60% of the total genetic variance
and 15% of the total phenotypic variance in brain weight in the ABXDF2
cross.
Retinal ganglion cell numbers range from 50,600 to 69,000 among
standard inbred strains of mice. I examined the contribution of
environmental and genetic factors to the variation in ganglion cell number
among 17 standard inbred strains, 26 BXD recombinant inbred strains, and two
F2 intercrosses. Variation in ganglion cell number
was not correlated with age, sex, or body weight, within any of the inbred
strains. However, ganglion cell number was significantly correlated with
brain weight across strains and within heterogeneous mice, accounting for
20% to 30% of the variance in ganglion cell number. Genetic variation was
evident from the significantly larger variance in ganglion cell number among
strains compared to within strains. A bimodal probability distribution of
ganglion cell number from 57 inbred strains, in addition to the estimate of
one to three genes modulating ganglion cell number, indicates that there are
genes with large effects on ganglion cell number. The heritability of
retinal ganglion cell number in the standard inbred mouse strains is 0.48.
These results indicate that it should be feasible to map some of the QTLs
modulating ganglion cell number among inbred strains.
I used the BXD recombinant inbred set and two F2
intercrosses (CCASF2, n = 112, 32CASF2,
n = 140) in linkage analysis, with composite interval mapping, to map
genes responsible for variation ganglion cell number. I mapped four
significant QTLs affecting ganglion cell number to Chrs 1, 7, 11, and 16.
The ganglion cell QTLs have been named Neuron number control 1, 2, 3,
and 4 (Nnc1, 2, 3, & 4). Nnc1 maps to Chr 11 at
57 cM and has a LOD score of 6.7. Nnc2 maps to Chr 7 at 65 cM and has
a LOD score of 5.9. Nnc3 maps to 82 cM on Chr 1 and has a LOD score
of 9.3, and Nnc4 maps to Chr 16 at 41.5 cM and has a LOD score of
6.0. Thyroid hormone receptor alpha (Thra) was identified as a
superb candidate gene for Nnc1. I tested Thra as a candidate
by comparing the ganglion cell number in transgenic mice carrying a null
transgene at the Thra locus with ganglion cell number from mice
carrying a wild-type Thra. The mice with the null Thra
transgene had significantly lower ganglion cell numbers compared to the
wild-type mice. The result supports Thra as a candidate gene for
Nnc1.
Finally, I examined the developmental mechanisms responsible for the
differences in ganglion cell among strains of mice. I estimated ganglion
cell production in strains with high ganglion cell number and strains with
low ganglion cell number by counting ganglion cells at postnatal day zero.
Approximately 77% of the variation among adult strains result from
differences in the production of ganglion cells. Thus, the variation in
adult ganglion cell number among inbred mouse strains results predominantly
from differences in cell production. Collectively, the results indicate that
some of the Nnc1, 2, 3, & 4, QTLs are likely to
modulate ganglion cell number by influencing cell production.
In summary, I have demonstrated, 1) the proportion of variance in brain
weight and ganglion cell number explained by genetic and non-genetic
factors, 2) the location of QTLs producing variation in brain weight and
ganglion cell number, and 3) the predominant mechanism generating variation
in ganglion cell number is cell production. Finally, of significance, the
mapping studies prove that it is possible to map the genes that are
responsible for both global and discrete quantitative variation within the
mouse brain.
Table of Contents
Chapter 1:
Introduction
Forward genetic approach*
Exploiting natural genetic variation*
Dissertation research*
Importance of neuron number*
Developmental control of neuron number*
Chapter 2:
Genetic and Environmental Control of Brain Weight Variation
Introduction*
Materials and methods*
Results*
Discussion*
Chapter 3:
Mapping Genes Controlling Variation in Brain Weight Using Recombinant Inbred
strains and F2 Intercross Progeny
Introduction*
Materials and methods*
Results*
Discussion*
Chapter 4:
Genetic and Environmental Control of Retinal Ganglion Cell Variation
Introduction*
Materials and methods*
Results*
Discussion*
Chapter 5:
Mapping Genes Controlling Variation in Retinal Ganglion Cell Number Using
Recombinant Inbred Strains and F2 Intercross
Progeny
Introduction*
Materials and methods*
Results*
Discussion*
Chapter 6:
Developmental Mechanisms Controlling Retinal Ganglion Cell Variation Among
Inbred Mouse Strains
Introduction*
Materials and methods*
Results*
Discussion*
Chapter 7:
Discussion
Nature of the QTL*
References
Vita
List of Tables
Table 2.1 Average brain weights for 27 standard inbred strains corrected
for sex, age, and body weight.
Table 2.2 Brain weight for Mus species and subspecies corrected
for sex, age and body weight.
Table 2.3 Average brain weight for F1 hybrids
and their parental strains.
Table 2.4 Inbred strain multiple regression analysis for brain weight.
Table 2.5 CCASF2 multiple regression analysis
for brain weight.
Table 2.6 32CASF2 multiple regression analysis
for brain weight.
Table 2.7 ABXD5F2 multiple regression analysis
for brain weight.
Table 2.8 AC3HF2 multiple regression analysis
for brain weight.
Table 2.9 C3HAF2 multiple regression analysis
for brain weight.
Table 2.10 BXD multiple regression analysis for brain weight.
Table 2.11 AXB multiple regression analysis for brain weight.
Table 2.12 BXA multiple regression analysis for brain weight.
Table 2.13 Heritability, brain and body weight correlation, and gene
number for BXD, AXB/BXA, and F2 intercross mice.
Table 3.1 Corrected brain weight and body weight data for BXD strains
and parentals, C57BL/6J and DBA/2J.
Table 3.2 Corrected brain weight and body weight data for AXB/BXA
strains and parentals, A/J and C57BL/6J.
Table 3.3 BXD strain distribution pattern for brain weight and genotypes
at five loci on Chr 11.
Table 4.1 Average ganglion cell number for 18 standard inbred strains.
Table 4.2 Average ganglion cell number in inbred representatives of wild
strains.
Table 4.3 Average ganglion cell population in heterogenous mice.
Table 4.4 Average ganglion cell number in F1
hybrids and parental strains.
Table 5.1 Corrected retinal ganglion cell number for BXD strains and
parents, C57BL/6J and DBA/2J.
Table 5.2 BXD strain distribution pattern for retinal ganglion cell
number and genotypes at five loci on Chr 11.
Table 6.1 Ganglion cell number and percentage cell loss.
List of Figures
Figure 2.1. Lineage chart of the genus Mus with fixed brain weights for
strains, species, and subspecies of mice.
Figure 2.2. Probability density distribution of corrected brain weights
for 27 inbred strains.
Figure 2.3. Probability density distributions of corrected brain weight
for recombinant progeny and their parental strains.
Figure 2.4 Regression of ABXD5F2 brain weight
and body weight&.
Figure 2.5 Polynomial regression of ABXD5F2
brain weight and age.
Figure 2.6 Regression of ABXD5F2 brain weight
and parity.
Figure 3.1 Microsatellite marker positions for ABXD5F2
intercross screening.
Figure 3.2 PCR amplified microsatellite polymorphic between A/J and BXD5
and separated by electrophoresis on a 3% agarose gel.
Figure 3.3 Permutation of BXD brain weight data for conditional
genome-wide significance testing.
Figure
3.4 Linkage map demonstrates the QTL Bsc1 on Chr 11 in the BXD
data set.
Figure
3.5 Linkage map demonstrates the QTL Bsc2 on Chr 7 in the AXB/BXA
data set.
Figure
3.6 Linkage map demonstrates the QTLs Bsc3 and Bsc4 on Chr
14 in the ABXD5F2 data set.
Figure 4.1 Method for estimating ganglion cell numbers. Ganglion cell
number for strains, species, and subspecies of mice.
Figure 4.2 Probability density of retinal ganglion cell number for 57
inbred strains.
Figure 4.3 Scatterplot of retinal ganglion cell number for CCASF2,
CCASF1, and parental strains.
Figure
5.1Linkage map demonstrates the QTL Nnc1 on Chr 11 in the BXD
data set.
Figure
5.2Linkage map demonstrates the QTL Nnc2 on Chr 14 in the BXD
data set.
Figure
5.3Ganglion cell numbers for transgenic mice carrying homozygous null
Nnc1 candidate genes (-1), heterozygous null (0) and wildtype genes (1).
Figure
5.4 Linkage map demonstrates the QTL Nnc3 on distal Chr 1 in the
CCASF2 data set.
Figure
5.5 Linkage map demonstrates Nnc4 on Chr 16 in the 32CASTF2
data set.
Figure 6.1 Bimodal distribution of adult ganglion cell averages for 60
inbred strains.
Figure 6.2 Cross-section of a neonatal optic nerve.
Figure 6.3 Regression of P0 and adult ganglion cell number averages for
ten strains.
Figure 6.4. Regression of numbers of cells that are lost (number at P0
minus the number at maturity) and adult ganglion cell number from our data.
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