Chapter 22 - Quantitative genetics: Traits with a continuous
distribution of phenotypes are called continuous traits (e.g.,
height, weight, growth rate, personality, learning ability, crop
yield, fat content, etc.). Continuous traits arise from effects of:
Multiple loci Pleiotropy (one gene has many effects) Epistasis
Variable expressivity and penetrance Environment (produces a range
of phenotypes) Examples of different distributions:
LognormalNormalExponential Continuous traits: Francis Galton and
Karl Pearson (late 1800s): Recognized that continuous traits are
statistically correlated between parents and offspring, but could
not determine how transmission occurs. Wilhelm Johannsen (1903):
Demonstrated that bean seed weight is partly heritable and partly
environmental. Sir Ronald Fisher: First to demonstrate
mathematically that Mendelian models of allele segregation apply to
multiple genetic loci. Types of questions studied in quantitative
genetics: How do genetics and the environment affect a trait? Which
and how many genes produce a set of phenotypes for a trait; where
in the genome are they located? Do some genes play a major role,
whereas other genes modify or play a small role? Do alleles
interact to produce additive or epistatic effects? How does
selection affect the trait; does it affect other traits? What types
of mating and selection produce desired phenotypes? Types of data
collected and analyses to consider: Sample size/randomization Type
of distribution (e.g., normal distribution) Mean, variance, and
standard deviation Correlation/regression Analysis of variance
(ANOVA) Quantitative trait loci (QTLs): QTLs =specific genomic
segments correlated with continuous phenotypic trait variation.
Perform Genome Wide Association Study (GWAS) 1.Cross inbred lines
with different phenotypes (homozygotes for different alleles at
most loci) to produce heterozygotes. 2.Self F 1 or cross to
parental lines to increase phenotypic variation and segregation of
traits. 3.Analyze F 2 with physical markers (microsatellites) that
correlate with phenotypic variation. 4.Create a linkage map.
5.Calculate components of phenotypic variance (V P ) due to genetic
effects (V G ) and components due to environment effects (V E ). V
P = V G + V E + 2COV G,E + V G x E Components of genetic variance:
1.Additive genetic variance (V A ): effects of alleles at two or
more loci contribute to phenotype. F1 will will appear intermediate
to the parental phenotypes for repeated test crosses. 2.Dominance
variance (V D ): effects of alleles are not strictly additive; must
consider how alleles interact in the heterozygote. F1 will resemble
one of the parental phenotypes. 3.Interaction variance (V I ):
accounts for epistatic interactions between two or more loci. F1
phenotype is unpredictable. V G = V A + V D + V I V P = (V A + V D
+ V I ) + V E + 2COV G,E + V G x E Calculate Lod Score: 1.Lod = log
of the ratio odds that two loci (or a locus and a trait) are linked
with a recombination factor (q) greater than 0 and less than z =
log 10 {Prob(data|q)/Prob(data|0.5)} 3.Lod score of (odds of
1000:1) or greater is regarded as acceptable evidence for linkage.
Figure 1. Graph of multipoint lod scores assuming heterogenity The
peak multipoint lod score of 3.85 is located between DXS1200 and
DXS297. Nature Genetics 20, (1998) Evidence for a prostate cancer
susceptibility locus on the X chromosome. Jianfeng Xu et al.
Timmerman-Vaughan et al Linkage mapping of QTLs for seed yield,
yield components and developmental traits in pea (Pisum sativum L.)
Chromosome map of human QTLs for plasma concentrations of HDL-C,
LDL-C and triglyceride levels The Jackson Laboratory Genome Scan
Search for islands of genetic differentiation in otherwise
undifferentiated genetic background. Alternative method for
searching for genes underlying functionally important traits. Does
not require crossing experiment, but rather perform genome scan
(e.g., next-generation sequencing) for two populations that differ
in a single environmental variable subject to strong selection.
Works best for two populations that are in migration-selection
balance equilibrium experiencing strong divergent selection and
high gene flow. Utilize measures such as Fst Examines linked
patterns of statistically correlated divergence in different genes,
which may result from correlated selection and/or divergence
hitchhiking through depressed recombination. Alternate
interpretations of outliers. Via S Phil. Trans. R. Soc. B 2012;367:
2012 by The Royal Society Relationship between the maximum FST that
can be maintained by DH at equilibrium and the map distance from a
selected gene, for two intensities of divergent selection (modified
from [27], for a population with Ne = 1000 and m = 0.001). Via S
Phil. Trans. R. Soc. B 2012;367: 2012 by The Royal Society Probable
DH regions in threespine stickleback. Via S Phil. Trans. R. Soc. B
2012;367: 2012 by The Royal Society Broad- and Narrow-Sense
Heritability: 1.Broad-sense heritability = h B 2 = V G /V P
2.Narrow-sense heritability = h N 2 = V A /V P *Broad-sense
heritability measures proportion of phenotypic variance among
individuals in a population that results from genetic differences.
*Narrow-sense heritability measures proportion of phenotypic
variance that results from additive genetic variance. *Narrow sense
heritability is what can be used to predict resemblance between
offspring and parents. *Heritability is a measure of variance and
is only meaningful for characteristics of a population (not the
individual). Example showing how to calculate narrow-sense
heritability using parent-offspring regression: Example showing
response to selection in artificial experiment: h 2 = R/S
