Download Quantitative Genetics and Whole Genome Approaches

BIO421
Advanced Genetics
Quantitative Genetics, continued
Lecture 16
Completed in lecture 15
I. We will finish with the outline from last time, then proceed to…
II. Mapping quantitative trait loci
A. Cross a strain with an extreme expression of the quantitative trait with a strain that has the opposite
extreme and genetic markers (like SNPs!!!)
B. Backcross the F1 to the parental strains
C. Examine the F2 phenotype and the genetic markers
D. Cosegregation between the trait and a marker or markers suggests the QTL is nearby
E. Examples using various methods
1. Backcrossing F1 to the parental strains and examining F2 directly
a. DDT resistance in Drosophila: in this example, a resistant fly strain was crossed with a
susceptible fly strain. The two strains had chromosomal markers for each chromosome. The
results showed that each of the two main autosomes and the X chromosome contain QTL for
DDT resistance.
b. Cholesterol profiles in mice: in this example, inbred mice strains with varying cholesterol
profiles were crossed. They also had SNPs when their genomes were compared. In this
example, QTL for cholesterol profiles were identified at various sites on the chromosomes.
1) This example demonstrated the value of using markers like SNPs, because they were able
to assay SNP markers at ~10 cM intervals on each of the chromosomes. This provides
much greater resolution than does the use of morphological genetic markers
2) This example also demonstrated that the cholesterol profile of the F2 can be more extreme
than either of the two parental strains – why???
2. Recombinant Inbred Lines – RILs: Here the F1 are interbred or selfed and the F2 are interbred or
selfed to create multiple inbred lines that are propagated at least 8 generations to homozygose the
chromosomes.
a. QTL for lettuce seed germination from Dr. David Still
1) Crossed L. sativa with L. serriola and created multiple RILs
2) Found that there was a large amount of variation in seed germination temperatures.
3) Mapped seed germination and other QTL to the chromosomes.
3. Nearly Isogenic Lines – NILs: Here the F1 are interbred, and only F2 with a given phenotype
(eg. germination at high temperature) are selected. The selected F2 are mated back to one of the
parental strains (ie. L. sativa) to start the process again. After 8 selection crosses, QTL for the
selected trait are backcrossed into one of the genomes. These QTL are easily identified using
SNPs.
F. Expression QTL – eQTL
1. Here, you are interested in QTL that affect the expression of a given gene or genes.
a. Perhaps you know that some phenotype (eg. seed germination) is more likely when a given
gene is expressed at a greater level.
b. You complete the same type of mapping cross using RILs, but the phenotype you are assaying
is the amount of transcript produced for the given gene.
1) Transcript abundance can be assayed via quantitative rt-PCR
G. QTL in humans – you cannot do crosses, so other methods must be used
1. Genome Wide Association Studies (GWAS)
a. Must determine genetic markers in many, many participants
1) SNP chips used for this. They are like microarrays, but they detect SNP variation
b. Must include many thousands of participants and assay SNPs and phenotypes
c. Can determine QTL, but limited by number of participants and genomic variation
1) Here you look for unique SNPs that associate with disease phenotypes
d. Examples
1) Study of QTL associated with migraines
2) Study of QTL associated with HIV viral load
e. What have we learned about complex diseases from GWAS
1) Compared to the “good old days” before the ability to assess millions of SNPs, we now
have discovered hundreds of loci linked to disease
2) These loci are primarily associated with a much reduced effect on the disease (low risk)
and much higher frequencies in the populations
3) A seeming majority map to regions that are non-coding
(i) Are they regulatory? Epigenetic or eQTL?
4) When we try to include these newly identified polymorphisms into a model for disease
risk, they only add a small amount to our ability to predict disease.
H. Familial Whole Genome Sequencing
1. Involves a small number of individuals from a single family with a genetic disorder
2. Sequence the genomes of all individuals
3. Identify SNPs compared to the reference genome
4. Make predictions about the chromosomal locations of the SNPs based upon disease inheritance
patterns (ie. dominance/recessive, common v. rare disorder)
5. Identify candidate genes.
6. Three studies:
a. Miller syndrome and Primary Ciliary Dyskinesia
b. Infantile Epileptic Encephalopathy
c. Autism
7. What have we learned from Whole Genome Sequencing
a. It is most apt to identify disease alleles when they are of the large effect variety
b. The actual mutation is identified
c. We can identify variants for subsequent studies.