HMB265H1 Lecture Notes - Lecture 15: Quantitative Trait Locus, Genetic Linkage, Association Mapping

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Published on 25 Nov 2012
Lecture 15
19.5 Mapping QTL in Populations with Known Pedigrees
The genes that control variation in quantitative (or complex traits) are known as qualitative trait loci
QTL have allelic variants that typically make relatively small, quantitative contributions to the phenotype
Can visualize the contributions of the alleles at a QTL to the trait value by looking at the frequency distributions associated
with each genotype at a QTL
When distributions overlap, cant determine genotype simply by looking at an individuals phenotype (as we can for genes
that segregate in Mendelian ratios)
QTL mapping has revolutionized our understanding of the inheritance of quantitative traits
Fundamental idea behind QTL mapping is that one can identify the location of QTL in the genome using marker loci linked
to a QTL
o Suppose you make a cross between 2 inbred strains P1 with a high trait value and P2 with a low trait value
o F1 can be backcrossed to P1 to create a BC1 population in which the alleles at all the genes in the 2 parental
genomes will segregate
o Marker loci such as SNPs or microsatellites can be scored unambiguously as homozygous P1 or heterozygotes for
each BC1 individual
o If there is a QTL linked to the marker locus, then the mean trait value for individuals that are homozygous P1 at the
marker locus will be different from the mean trait value for the heterozygous individuals
o Based on such evidence, one can infer that a QTL is located near the marker locus
The basic method
For a certain marker,
o If the means for the genotypic classes are very close to the overall mean, there is no QTL affecting the phenotype
near that marker
o If the means for the genotypic classes are quite different from the overall mean and from each other, there is a
QTL affecting fruit weight near that marker
How different do they need to be before we declare that a QTL is located near a marker?
o The statistical analysis involved calculating the probability of observing the data given that there is a QTL near the
marker locus and the probability of observing the data given that there is not a QTL near the marker locus
o    
Researchers report the of the odds, or the Lod score
If there is a QTL near the marker, then the data were drawn from two underlying distributions (each has its own mean and
If there is no QTL, then the data were drawn from a single distribution for which the mean and the variance are those of the
entire population
Lod scores can be calculated for points between the markers
o This can be done by using the genotypes of the flanking markers to infer the genotypes at points between the
o The odds equation incorporates this uncertainty when one calculates the Lod score at points between the markers
The Lod scores can be plotted along the chromosome shows peaks and flats
o The null hypothesis is that there is NOT a QTL at a specific position along the chromosome
o The greater the Lod score, then the lower the probability under the null hypothesis
o Where the Lod score exceeds the threshold value, then we reject the null hypothesis in favour of the alternative
hypothesis that a QTL is located at that position
QTL mapping can be done with F2 populations and other breeding designs. Advantages:
o One gets estimate of the mean trait valued for all 3 QTL genotypes
o With these data, one can get estimates of the additive and dominance effects of the QTL
What can be learned from QTL mapping?
o The number of QTL (genes) affecting a trait
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o The genomic locations of these genes
o The size of the effects of each QTL
o The mode of gene action for the QTL (dominant vs additive)
o Whether one QTL affects the action of another QTL (epistatic interaction)
From QTL to gene
The resolution of QTL mapping is on the order of 1 to 10 cM, the size of a region that can contain 100 or more genes
To go from QTL to a single gene requires additional experiments to fine map a QTL
To do this, the researcher creates a set of genetic homozygotes (aka lines), each with a crossover near the QTL
o These stocks or lines differ from one another near the QTL, but they are identical to one another (isogenic)
throughout the rest of their genomes
o Lines that are identical throughout their genomes except for a small region of interest are called congenic or
nearly isogenic lines.
The isolation of QTL in an isogenic background is critical because only the single QTL region differs between the congenic
Thus, the use of congenic lines eliminates the complications caused by having multiple QTL segregate at the same time
19.6 Association Mapping in Random-Mating Populations
Association mapping is a method for finding QTL in the genome based on naturally occurring linkage disequilibrium
between a marker locus and the QTL in a random mating population
o Allows researchers to directly identify the specific genes that control the differences in phenotype among
members of a population
Association mapping is now routinely used to scan the entire genome for genes contributing to quantitative variation
o This type of study is known as a genome-wide association study
o Major adv: candidate genes are not required since one is scanning every gene in the genome
Other advantages
o Since it is performed with random-mating populations, there is no need to make controlled crosses or work with
human families with known parent-offspring relationships
o It tests many alleles at a locus at once (QTL mapping can only do 2)
o Can lead to the direct identification of the genes at the QTL without the need for subsequent fine-mapping studies
Possible because the SNPs in any gene that influences the trait will show stronger associations with the
trait than SNPs in other genes
The basic method
SNPs (or other polymorphisms) that are close to each other tend to be in strong disequilibrium
o Those that are farther apart are in weak or no disequilibrium
Genomes also tend to have recombination hotspots, points where crossing over occurs at a high frequency
Hotspots disrupt linkage disequilibrium such that SNPs on either side of the hotspot are in equilibrium with each other
SNPs that are not separated by a hotspot form a haplotype block of strongly correlated SNPs
An SNP could affect phenotype by causing an aa change or affecting gene expression
o Any SNPs that directly affect a phenotype are called functional SNPs
When the SNP genotypes are correlated (in disequilibrium), then the trait values will be correlated
A statistical test is performed separately on each SNP and the P values plotted along the chromosome
o The null hypothesis is that the SNP is not associated with the trait
o If the P value for an SNP falls below 0.05, then the evidence for the null hypothesis is weak and we will favour the
alternative hypothesis that the different genotypes at the SNP are associated with different phenotypes for the
o The P values are plotted using an inverse scale such that the higher up the y-axis, the smaller the value
Association mapping does not actually prove that a gene or an SNP within a gene affects a trait
o It only provides statistical evidence for an association between the SNP and the trait
o Formal proof requires molecular characterization of the gene and its different alleles
GWA, genes, disease, and heritability
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