Linking Genotype to Phenotype

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Linking Genotype to Phenotype
Lecture 4
GCATCCATCTTGGGGCGTCCCAATTGCTGAGTAACAAATGAGACGC
TGTGGCCAAACTCAGTCATAACTAATGACATTTCTAGACAAAGTGAC
TTCAGATTTTCAAAGCGTACCCTGTTTACATCATTTTGCCAATTTCG
CGTACTGCAACCGGCGGGCCACGCCCCCGTGAAAAGAAGGTTGTT
TTCTCCACATTTCGGGGTTCTGGACGTTTCCCGGCTGCGGGGCGG
GGGGAGTCTCCGGCGCACGCGGCCCCTTGGCCCCGCCCCCAGTC
ATTCCCGGCCACTCGCGACCCGAGGCTGCCGCAGGGGGCGGGCT
GAGCGCGTGCGAGGCGATTGGTTTGGGGCCAGAGTGGGCGAGGC
GCGGAGGTCTGGCCTATAAAGTAGTCGCGGAGACGGGGTGCTGGT
TTGCGTCGTAGTCTCCTGCAGCGTCTGGGGTTTCCGTTGCAGTCCT
CGGAACCAGGACCTCGGCGTGGCCTAGCGAGTTATGGCGACGAAG
GCCGTGTGCGTGCTGAAGGGCGACGGCCCAGTGCAGGGCATCAT
CAATTTCGAGCAGAAGGCAAGGGCTGGGACGGAGGCTTGTTTGCG
AGGCCGCTCCCACCCGCTCGTCCCCCCGCGCACCTTTGCTAGGAG
CGGGTCGCCCGCCAGGCCTCGGGGCCGCCCTGGTCCAGCGCCCG
GTCCCGGCCCGTGCCGCCCGGTCGGTGCCTTCGCCCCCAGCGGT
GCGGTGCCCAAGTGCTGAGTCACCGGGCGGGCCCGGGCGCGGG
GCGTGGGACCGAGGCCGCCGCGGGGCTGGGCCTGCGCGTGGCG
GGAGCGCGGGGAGGGATTGCCGCGGGCCGGGGAGGGGCGGGGG
CGGGCGTGCTGCCCTCTGTGGTCCTTGGGCCGCCGCCGCGGGTC
TGTCGTGGTGCCTGGAGCGGCTGTGCTCGTCCCTTGCTTGGCCGT
GTTCTC
Much of the genome remains to be annotated
Non-coding
Protein Coding
Human genome
repeats
Ways to link genotypes to
phenotypes

Forward genetics –
Find the gene or set of
genes responsible for a
given phenotype

Reverse genetics –
Characterize the
phenotypic effect of a
gene by manipulating it
in the genome.
Tierney, M.B. and Lamour, K.H. 2005.
Ways to link genotypes to
phenotypes

Forward genetics –
Find the gene or
set of genes
responsible for a
given phenotype
 Random
mutagenesis (point
mutations or
insertions) followed
by breeding,
isolating individuals
with a particular
phenotype and
identification of the
mutational changes.
Lindsay MA, Nature Reviews Drug Discovery 2, 831-838
Ways to link genotypes to
phenotypes

Forward genetics –
Find the gene or set of
genes responsible for a
given phenotype

Reverse genetics –
Characterize the
phenotypic effect of a
gene by manipulating it
in the genome.
 Directed deletion or
point mutations
 Gene knockdown by
RNA interference
 Over-expression of a
gene
Lindsay MA, Nature Reviews Drug Discovery 2, 831-838
High Throughput
Genetics in Yeast S.
cerevisiae


Nearly all non-essential
genes were individually
deleted and replaced with
a drug resistance gene
containing two DNA
barcodes.
The barcodes uniquely
identify each knockout
strain and allow pools of
mutant strains to be
analyzed simultaneously.
Boone et al., Nature Rev Genetics. 2007, 8 (6) pp. 437-49
Parallel Phenotypic Analysis in
Yeast
Rich Media
(60x generations)
Minimal Media
(60x generations)
Winzeler et al., Science, 1999 vol. 285 (5429) pp. 901-6
Additional lessons from yeast
deletion collection

20% of the yeast genes (~1000)
are essential – deletion is lethal

13% of gene deletions showing
growth defects in minimal
media were unannotated – thus
providing functional annotation
to new genes

Surprise 1: growth fitness does
not correlate with gene
expression levels

Surprise 2: more than 80%
(5000) yeast genes are nonessential. Why?
Giaever et al., Nature 2002 vol.
418 (6896) pp. 387-91
Genetic networks

Gene products function
in networks and
pathways

Within the genetic
network, many genes
function redundantly or
interact with each other
in complex manner

Synthetic lethal assays
provide a way to
interrogate genetic
interactions
Synthetic
Genetics Arrays
Boone et al., Nature Rev Genetics. 2007, 8 (6) pp. 437-49
Synthetic Genetics Arrays reveal
gene pathways and network
modules
Tong et al., Science 2004 vol. 303 (5659) pp. 808-13
Synthetic Genetics Arrays reveal
gene pathways and network
modules
Boone et al., Nature Rev Genetics. 2007, 8 (6) pp. 437-49
Small world of genetic
interactions



Most genes
genetically interact
with small number
of other genes
A small number of
genes interact with
a large number of
genes
A short path exists
between any pair
of genes
Tong et al., Science 2004 vol. 303 (5659) pp. 808-13
A more quantitative view of
genetic interactions
Beltrao et al., Cell 2010 v141 (5) pp. 739-45
Beltrao et al., Cell 2010 v141 (5) pp. 739-45
A more quantitative view of
genetic interactions
Collins et al., Nature 2007 vol. 446 (7137) pp. 806-10
Summary

Complete genome sequences and gene catalogues have
enabled the study of genetic interactions in yeast.

Genetic genetic interactions cluster as functional modules
such as protein-protein complexes.

Genetic networks highlight the deep intrinsic buffering of
cellular function through redundant or overlapping
pathways.

A minority of genes are essential, and these define hubs
of activity that can in some cases extend beyond a given
functional module to influence and even coordinate
multiple cellular processes.

Given this interactional complexity, that single genes
rarely specify a phenotype in its entirety.
What about mammals?

An international knock out mouse project (KOMP) is underway.

A conditional knockout resource for the genome-wide study of mouse
gene function has been developed (>9,000 conditional targeted alleles)

These ES cells would require germline transmission and subsequent
breeding to assess gene function.
Skarnes et al., Nature 2011 vol. 474 (7351) pp. 337-42
What about mammals?
In the meanwhile, RNA interference has
been used to knockdown almost every
gene.
 The assays are typically done using high
throughput, high content imaging tools.
 Some applications employ pooled shRNA
constructs

Kim & Rossi, Nature Rev Genetics, 2007
Haploid mouse ES cells
Elling et al. Cell Stem Cell 2011, 9 (6) pp. 563-74
Forward genetic screening
using haploid mouse ES cells
Elling et al. Cell Stem Cell 2011, 9 (6) pp. 563-74

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