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Applications in Bioinformatics,
Proteomics, and Genomics
SNPs (II)
J. Gray (UT)
[email protected]
Previous lecture
- Introduction to SNPs
Todays lecture
1: Mapping complex traits using SNPs
2: Explanation of Association analysis
3: Example of complex trait mapping
Using SNPs to find a gene linked to retinal
dystrophy Janecke et al., 2004
4: Example of trait mapping through whole
genome sequencing (WGS) Lupski et al 2010
5: WGS to determine drugs suitable for cancer
treatment Iyer et al 2012
6: Homework
1: Mapping complex traits using SNPs
Mapping Complex Traits Using SNPs
Genome wide association studies (GWAS) have
been framed by the Common Disease/Common
Variant (CD/CV) hypothesis which states that…..
The model that complex disease is largely attributable to a
moderate number of common variants, each of which
explains several per cent of the risk in ethnically diverse
populations
Common variants differ from
very rare high-risk alleles
which have been mainly
discovered through pedigree
analysis (of related individuals)
CDCV versus alternative models
The CDCV model has now been refuted in light of the
‘missing heritability problem’: the observation that loci
detected by GWASs explain almost without exception a
small minority of the inferred genetic variance…
(The contribution of genotypic differences among
individuals to phenotypic variation).
expected signatures from
GWA studies for CDCV
model of disease
Gibson G., 2012 Rare and common variants: twenty arguments
Nature Reviews - Genetics 13:125-145
CDCV versus alternative models
The Infinitesimal model: many variants of small effect.
If half a dozen common variants explain 10% of risk in the
population, the remainder is attributable to a hundreds or
thousands of variants that each explain considerably less
than 1% of disease risk (including rare variants). The
contribution of some genes is too small to measure.
expected signatures from GWA
studies for infinitesimal model
of disease
Gibson G., 2012 Nature Reviews - Genetics 13:125-145
CDCV versus alternative models
The Rare allele model: many rare alleles of large effect.
The alternative view is that most of the variance for
certain complex diseases is due to moderately or highly
penetrant rare variants, the allele frequency of which is
typically <1%, most of which are recently derived alleles in
the human population.
expected signatures from GWA
studies for rare allele model of
disease
Gibson G., 2012 Nature Reviews - Genetics 13:125-145
CDCV versus alternative models
The Broad sense heritability model: non-additive GXG and
GXE interactions and epigenetic effects.
Proponents of this model point to a long history of
detection of genotype-by-genotype interactions (aka
epistasis) and genotype-by-environment interactions in
model organism quantitative genetic research
expected signatures from GWA
studies for broad sense
heritability (G X E) model of
disease
Green and orange represent
different environments
Gibson G., 2012 Nature Reviews - Genetics 13:125-145
Reconciliation: There are joint effects
of rare and common variants
high
Enzyme
activity
Different combinations of
rare and common variants
can tip the balance from
health to disease states
Gibson G., 2012 Nature Reviews - Genetics 13:125-145
low
Reconciliation: There are joint effects
of rare and common variants
Gibson G., 2012 Nature Reviews - Genetics 13:125-145
Figure 4 | Joint effects of rare and common variants. A
straightforward reconciliation of the effects of rare and common
variants supposes that pervasive common variation influences the
expression and activity of genes in pathways, establishing the
background liability to disease that is then further modified by rare
variants with larger effects. In this hypothetical example of central
metabolism, standing variation results in some individuals having lower
flux than others (left versus right; colored boxes imply enzyme
activity differences from low activity (red shading) to high activity
(green shading)), but according to standard biochemical theory,
systems evolve such that most variation is accommodated within the
healthy range. The impact of a rare variant that knocks out one copy
of the enzyme indicated by the cross is conditional on this liability,
pushing the individual on the left beyond the disease threshold,
whereas the individual on the right can accommodate the mutation,
given higher activity elsewhere in glycolysis
2: Explanation of Association analysis
Whatever the balance is between rare and
common variants genome wide association studies
have been successful in uncovering thousands of
genomic variants associated with diseases.
The advent of very high-density microarrays and
WGS enable us to capture genome-wide variation
on a huge scale.
Association studies seek to correlate that
variation with the underlying genetic cause of
complex phenotypes (such as disease, height,
etc)..but how?
Brief list of SNP-associated human diseases
Int. J. Mol. Med. 2003 11:379-382
Many GWAS studies have been performed
to link SNPs to complex phenotypes
https://www.gwascentral.org
GWAS Central provides a centralized compilation of summary level
findings from genetic association studies, both large and small.
They actively gather datasets from public domain projects, and
encourage direct data submission from the community
Beck et al 2014. GWAS Central: a comprehensive resource for the comparison and
interrogation of genome-wide association studies. European Journal of Human Genetics
advance online publication 4 December 2013; doi: 10.1038/ejhg.2013.274
SNPs near genes are associated with
disease traits
The high frequency and even distribution of SNPs across
the genome make them very useful as markers for gene
mapping studies – especially of complex traits
DNA mutations are occasional leading to an association
among SNPs along chromosomes – the presence of 1 variant
provides information about the presence of another –
(linkage disequilibrium LD)
SNPs in 5′ UTR of genes show the largest # of associations
SNPs related to exons and the 3′UTR are also enriched.
SNPs related to introns are only moderately enriched,
Intergenic SNPs show a depletion of associations relative to
the average SNP
Schork et al 2013 PLOS Genetics DOI: 10.1371/journal.pgen.1003449
Closely linked SNPs may be in linkage
disequilibrium
If two alleles (or two SNPs) tend to be inherited more
often than other pairs then we say that they are in
“linkage disequilibrium” (LD) (high LD)
Linkage disequilibrium is the non-random association
of alleles at two or more loci, that descend from single,
ancestral chromosomes
Imagine if a particular population has a higher than
average incidence of Alzheimer’s disease
If one could track SNPs that are in LD and correlate
with disease phenotype we would have markers for loci
that play a role in Alzheimers disease
To map a gene underlying a
trait one needs to find
segment of chromosome
(haplotype) that is linked
with a mutation causing
predisposition to a certain
trait (circled patients in
diagram)
e.g. segment A in two
affected offspring in this
diagram
In this segment SNPs may
be in linkage
disequilibrium (LD)
Many
Generations
Important points about LD
1. Population genetics describes the way
mutation, recombination, natural selection
and demographics affect patterns of LD
2. There is no a priori way to predict the LD
pattern in a particular genomic region
3. LD must be empirically assessed in a
particular
genomic
region
using
appropriately chosen samples
4. SNPs less than a few kb apart may have
weak LD (e.g. if there is high recombination)
(LD) and haplotype analysis
Example: 2 SNPs linked to a monogenic disease
Notice that the two populations have two
frequencies of the three possible genotypes.
Example of a more complex trait where 3
different genes influence the phenotype
2 populations are examined
The phenotype is rated on a 0-7 scale
Individuals are genotyped using three loci
(6 SNPs) – a total of 27 possible
genotypes – (color coded to aid
visualization of genotypes)
Case is more typical of a polygenic trait
that can be mapped to quantitative traits
loci (QTLs)
X’-Y’ locus may have a dominant contribution (blue)
a’-b’ may have a minor recessive contribution (red)
Locus 1 Locus 2 Locus 3
Pop 1
Pop 2
1’-2’ locus may also have a dominant contribution (red)
Strongest phenotype when X’-Y’ and 1’-2” in presence
of a’-b” homozygote (see next slide)
Score
Example
is
still
rather
simple
– in reality many loci are used and many
SNPs per locus
Study is population dependent – population 2
has a greater frequency of the disease –
more likely to discover informative “ linkage
disequilibrium” (LD)
How many loci contribute to the phenotype?
Which SNPs are linked to disease causing
alleles ?
How to measure association of
SNPs with each other (LD)
There are several measures of LD
If 2 alleles at each of 2 SNP loci and frequencies
of alleles are written p1, p2, q1, q2, with hapltype
frequencies written as p11, p22, p12, and p21
Magnitude of LD is D= (p11)(p22) – (p12)(p21)
And D’ = D/Dmax
Another
measure
r2 = D2|pApapBpb
of
LD
is
r2
Compared with |D ’ |, r2 values are lower and less
affected by sample size or allele frequency
Example of LD Calculation
Suppose there are two genes on Chromosome 5, each
with two alleles
SNP1
SNP2
ACTGGTAT ………………….GATCAACCAG
Allele 1
Allele 2
ACTCGTAT ………………….GATCATCCAG
Step 1. Calculate allele frequencies
Step 2. Calculate haplotype frequencies (GA, GT, CA, CT)
Example of LD Calculation
Step 3. Linkage Equilibrium
When haplotype frequencies are equal to the product of their
corresponding allele frequencies, it means the loci are in linkage equilibrium
Step 4. Linkage Disequilibrium
We can deduce linkage disequilibrium for each haplotype as the deviation
of observed haplotype frequency from its corresponding allelic frequencies
expected under equilibrium.
D = (p11)(p22) – (p12)(p21)
Example of LD Calculation
Step 5. Calculation of Linkage Disequilibrium (D)
If allele frequencies of p1 and q1 are both 0.5 (thus p2 and q2 are also 0.5)
and equilibrium occurs (haplotypes GA, GT, CA, CT all exist in popn)
P11 = p1q1 = 0.5 x 0.5 = 0.25
P22 = p2q2 = 0.5 x 0.5 = 0.25
P12 = p1q2 = 0.5 x 0.5 = 0.25
P21 = p2q1 = 0.5 x 0.5 = 0.25
D = (P11)(P22)-(P12)(P21)= (0.25()0.25)- (0.25()0.25) = 0
If allele frequencies of p1 and q1 are both 0.5 but is complete non-random
association with equal allele frequencies at all loci (only haplotypes GA, and
CT exist in popn)
D = (P11)(P22)-(P12)(P21)= (0.5)(0.5)- (0)(0) = .25
P11 = p1q1 +D = 0.25 + D = 0.5
P22 = p2q2 +D = 0.25 + D = 0.5
P12 = p1q2 -D = 0.25 - D = 0
P21 = p2q1 -D = 0.25 - D = 0
If D= 0.25 then D’ = D/Dmax = .25/.25 = 1 (where Dmax= min p1q2 or p2q1)
And r2 = D2/(p1p2q1q2) = (0.25)2/(0.5x0.5x0.5x0.5) = .0625/.0625 = 1
LD is lower in more diverse populations
and vice versa
Mean linkage disequilibrium (D ’ ) as a function of
physical distance (kb) in samples from three ethnic
groups
(Trends in Genetics, 2002 18:1 p19-24)
LD is lower in more diverse populations
and vice versa
Mean linkage disequilibrium (r2) as a function
of physical distance (kb) in samples from
different ethnic groups
(Trends in Genetics, 2002 18:1 p19-24)
Notes about Genome Wide Association
Studies (GWAS)
1. Association analysis differs from more traditional LD
analysis in that it compares the frequency of a set of
alleles (tagSNPs) between “unrelated” patients and
healthy controls.
2. It is easier to recruit larger numbers of unrelated
affected individuals than it is to collect large numbers
of pedigrees
3. Regions around a shared marker (tagSNP) are smaller
between unrelated individuals so larger populations are
required
4. Large SNP genotyping arrays and WGS now enable whole
genome scanning to be performed to find tagSNPs
3: Examples of complex trait mapping
Mutations in RDH12 encoding a photoreceptor cell
retinol dehydrogenase cause childhood-onset
severe retinal dystrophy.
Janecke et al., 2003.
Nature Genetics 36: p850-854
Autosomal recessive childhood-onset severe
retinal dystrophy.
What was the aim of this research ?
How did they set about answering their questions ?
What is autosomal recessive childhood-onset severe
retinal dystrophy ?
First described in 1869 by T. Leber and is also called
Leber congenital amaurosis type III (LCA)
Groups some of the most common causes of
genetically inherited childhood blindness.
Starts shortly after birth, involuntary eye movement
(nystagamus), sluggish pupillary response
Classic case of two-locus trait - since unaffected
children can be born from 2 affected parents
Starting point: Identifying three
consanguineous populations
Affected (living) individuals in black. Those with a line
underwent an opthalmoscopy. Similar phenotype and from
a common geographic area suggests a probable unknown
common ancestor to all cases
Mapping 10K Array
The GeneChip® Mapping 10K Array offers ability
to assay over 10,000 genotypes on a single array
No need for locus-specific PCR.
Requires only 250 ng of DNA for each sample
An average of one SNP every 210 kb on genome
(about 5cM resolution)
Automated genotype calling ( 99.6% accuracy)
Extensive SNP annotations in the NetAffx™
Analysis Center
SNP assay method - sequence-specific
based on hybridization
They genotyped DNA samples from 10 affected
individuals and 9 carriers from the 3 families
10,894 autosomal markers, 301 X-linked SNPs
used
Used GDAS v2.0 analysis software to call
genotypes. Checked for errors using PedCheck
software. Used Merlin and Genehunter programs
to reconstruct haplotypes.
Found 10 SNPs that were homozygous in affected
individuals and heterozygous in carriers - in an
interval of 2.86 Mb on chr 14q23.3- q24.1
This region overlapped with an interval
previously associated with LCA3
Examined region and found 29 genes - one of
which was RDH12 - encoding retinol
dehydrogenase -expressed in neuroretina
These enzymes help covert Vitamin A to 11-cisretinal.
Gene has 7 exons and makes a 316 aa protein
Could this be the gene that is disrupted in
affected individuals ?
Did PCR to amplify all segments of RDH12 gene
from affected individuals.
Looked for segments containing mutations using
denaturing HPLC.
DNA with a single base
mutation and wild-type
DNA are heated and then
cooled slowly to form a
mixture of hetero- and
homoduplexes. These are
easily and quickly
resolved by HPLC.
They detected four SNPs in the RDH12 gene two of them in exons.
bp677 A-G transition in exon 6 causes a
Tyr226-Cys226 substitution (only in affected
individuals)
bp482 G-A transition in exon 6 causes a
Arg161-Glu226 substitution (also found in
unaffected controls)
Found bp677 A-G transition in two more
unrelated affected individuals in western Austria
Other non-Austrian individuals found to be
homozygous for the following mutations in RDH12
Origin
Mutation
German
806 deletion of CCCTG in exon 6
Turkey
565C-T causing Q189X
American 146T-C (T49M) and 184C-T (R62X)
(on different alleles)
Found that all
individuals with
mutations in RDH12
had retinal dystrophy
starting at age 24ys - legal blindness
by age 18-25.
Fig 2b. Attenuation of arterioles,
peripheral pigment deposits in fundus
of 5 yr old patient.
Then they did a series of biochemical
experiments to prove the association that they
had found ....
Assayed normal and
mutant enzymes
expressed in vivo in
COS-7 cells.
The C226 mutation lost
nearly all activity.
The M49 mutation
seemed to produce
more than the wildtype
and also the back
reaction retinol-to
retinal (Fig 3b and c)
Forward reaction
(retinal to retinol)
Forward reaction
(retinol to retinal)
The M49 mutation seemed to produce more than
the wildtype and also the back reaction retinolto retinal (Fig 3b and c)
Deglycosylated M49
protein shows a
similar protein
pattern to WT (on
western blot) - but
a lower amount of
glycosylation
Anti-RDH12
(loading control
with GAPDH on
lower panel)
Anti-GAPDH
Summary of paper
•Using 10k SNP assay it was possible to quickly
identify a candidate locus underlying genetic
blindness in a local population
•Success was accelerated due to previous work
on RDH enzyme - but could also have been
successful without this work
•Study is convincing because of follow up
genetic, biochemical, and cellular studies.
•Challenge for the future – how to use this
knowledge to stop children from becoming blind?
Development of Targeted Diagnostic Panels
New protocols
enrich for exon
regions of genes
(here 163 genes)
Followed by high
throughput
sequencing
Tested 179
patients.
Found 45% novel
mutations in
genes.
TruSeq Exome Enrichment Workf!ow – requires only 1ug
DNA and spans 21,000 genes of interest (62MB coverage)
Wang, Xia; et al. 2013 Comprehensive molecular diagnosis of 179 Leber
congenital amaurosis and juvenile retinitis pigmentosa patients by targeted
next generation sequencing. Jour. of Med Genetics 50, 10: 674-688
From gene discovery to gene therapy
RPE65 is the isomerohydrolase essential for regeneration
of 11-cis retinal, the chromophore of visual pigments
Now are using gene therapy to introduce normal genes into
the retinal cells of patients with this form of LCA – with
some success.
Now looking at treating younger patients
Bainbridge J. W., et al. 2008 Effect of gene therapy on visual function in
Leber’s congenital amaurosis. N. Engl. J. Med. 358, 2231–2239.
Cideciyan A. V., et al. 2009 Human RPE65 Gene therapy for Leber congenital
amaurosis: persistence of early visual improvements and safety at one year.
Hum. Gene Ther. 20, 999–1004.
Annear, M et al. 2013 Successful Gene Therapy in Older Rpe65-Deficient Dogs
Following Subretinal Injection of an Adeno-Associated Vector Expressing
RPE65 Human Gene Therapy 24, 10: 883-893
RPE65 gene therapy in humans – 3yr outcome
15 patients form 3 to
29 years have been
injected with
adenovirus vector with
RPE65 gene
3 years later retinal
degeneration continues
but visual improvement
is still maintained.
Future attempts aim to
also decrease retinal
degeneration
Cideciyan A. V., et al. 2012. Human retinal gene therapy for Leber congenital
amaurosis shows advancing retinal degeneration despite enduring visual
improvement Proc Natl Acad Sci USA 110(6):E517–E525
and http://www.pnas.org/content/110/19/E1706.full.pdf+html.
Functional rescue in the rd12 mouse retina after 7m8mediated RPE65 gene transfer.
Directed evoluton of Adenovirus vector to improve gene
delivery to retinal cells
Deniz Dalkara et al. In Vivo−Directed Evolution of a New Adeno-Associated
Virus for Therapeutic Outer Retinal Gene Delivery from the Vitreous
Sci Transl Med 5, 189ra76 (2013); DOI: 10.1126/scitranslmed.3005708
Other uses of SNPs
Molecularly characterize all blood
group variants for any individual –
optimize blood transfusions and
transplants
Characterize
predispositions
to
cancer, heart disease, Alzheimers,
side effects of drugs etc.....
Map traits in crop and animal species
for inclusion in breeding programs
Success in complex trait mapping
1:
The putative disease gene is located in a
chromosomal region that cosegregates with the
disease in affected families
2:
This region contains multiple independent
mutations that are perfectly associated with
disease status in the families
3:
the characteristics of the mutations
obviously alter protein function in relation to
the disease phenotype
Cautionary notes about
association analyses
In genetic associations of "common diseases"
there is a very low "prior probability" of
detecting TRUE positives
a P value of 10-5 can still be false !
Other problems include
-selection biases in sample collection,
-genotyping errors,
-population substructure,
-subgroup analysis.
-possible complex GXG or GXE interactions
-
4: Example of trait
mapping through whole
genome sequencing
(WGS)
Lupski et al 2010
James R. Lupski,
M.D., Ph.D.
Baylor College of
Medicine
Lupski, J. et al., 2010 Whole-Genome Sequencing in a
Patient with Charcot–Marie–Tooth Neuropathy (CMT)
New England Jour Med 362:13 p 1181
What are the causes of rare
neurological diseases?
Neurodegeneration can result from subtle mutations acting over prolonged
time periods in tissues that do not generally regenerate
Linked to:
1) conformational changes causing prion disease,
2) the inability to degrade accumulated toxic proteins e.g. amyloidopathies,
α-synucleinopathies,
3) alteration in gene copy number (CNV) and/or expression levels through
mechanisms such as uniparental disomy (UPD), chromosomal aberrations
(e.g., translocations), and submicroscopic genomic rearrangements including
duplications, deletions, and inversions.
Charcot-Marie-Tooth (CMT) Phenotypes
39 different genes linked to CMT neurodegeneration
but only clinical tests available for 15 – none of these
applied to the family in question
Charcot-Marie-Tooth (CMT) Disease
We identified a family with a recessive
form of Charcot–Marie–Tooth disease for
which the genetic basis had not been
identified.
This is a common inherited
disorder that affects peripheral nerves
We……….
1: sequenced the whole genome of the
proband
2: identified all potential functional
variants in genes likely to be related to
the disease, and
3: genotyped these variants in the
affected family members.
Charcot-Marie-Tooth Disease
Used
SOLiD
Sequencing
(Sequencing by Oligonucleotide
Ligation
and
Detection
–
performed by Applied Biosystems)
Its accuracy in sequencing 50base reads is estimated at
approximately 99.94%.
Yield of 89.6 Gb of sequence
data, representing an average
depth of coverage of
approximately 30 times per
base.
No copy number variants in
CMT related genes
Charcot-Marie-Tooth Disease
About a half million new SNPs were uncovered
compared to the 7 previously sequenced human
genomes – a high rate of discovery of new SNPs at a
relatively low cost.
Charcot-Marie-Tooth Disease
Found 159 coding region
SNPs
associated
with
Mendelian Diseases
Looked at 40 genes linked
to neuropathy – in these
were 54 coding sequence
SNPs out of 3148 putative
SNPs – 2 of these were in
the SH3TC2 locus – 1
known R954X and 1 novel
Y169H
Genetic Pedigree found in this Study
New Y169H
allele – Y is
conserved in
animals
Looking forward……
In the “old” days -- meaning last week -- experts
would have had to suspect which disease the patient
had, then hone in on the area of the genome thought
to be associated with the disorder.
Even then, the results could be far from certain.
"The breakthrough is that now we would be able to make
this diagnosis without having any preconceived idea that
the patient had Charcot-Marie-Tooth disease," Marion
said.
Cost (About $50,000 (in 2010) compared to current
clinical tests in only 15 genes at a cost of $15,000.
“
5: WGS to determine
drugs suitable for
cancer treatment
Iyer et al., 2012
David B. Solit M.D.,
Memorial SloanKettering Cancer
Center
Iyer, G. et al., 2012 Genome Sequencing
Identifies a Basis for Everolimus Sensitivity.
Science 338 p 221
News feature:
http://www.reuters.com/article/2013/09/15/healthcancer-superresponders-idUSL2N0GN20120130915
Genetic Basis for “outlier”
Cancer patients ?
Why do some patients
respond
to
drug
treatment and others
not?
Clinical
Trial
of
Everolimus®
on
45
patients with bladder
cancer.
All died except 1 –
whose metastatic tumor
cleared.
Computed tomography images of the index patient shows
complete resolution of metastatic disease (arrows).
WGS of Tumor
Why did the one patient
respond to Everolimus?
Performed WGS on the
tumor and blood samples
17,136 somatic missense
mutations and small indels
(mutation rate of 6.21 per MB),
140 were non-synonymous
mutations within proteincoding or nc RNA regions of
the genome.
Somatic abnormalities in the outlier responder’s genome included (from
outside to inside) CNVs; mutations at ~10-Mb resolution; regulatory,
synonymous, missense, nonsense, nonstop, and frameshift indel mutations
(black, orange, red, green,and dark green); and intra- and interchromosomal
rearrangements (light and dark blue).
A 2bp frameshift in TSC1 gene correlated
best with tumor shrinkage
Some other patients that exhibited tumor regression had
mutations in TSC1. Alterations in TSC1 has been associated
with mTORC1 dependence in preclinical models
Best overall response of 14 sequenced trial patients. Negative values
indicate tumor shrinkage (red line, threshold for partial response).
Gradient arrow, patient with rapid progression in bone.
Lessons learned from this study
1. Suggests that mTORC1-directed therapies may be most
effective in cancer patients whose tumors harbor TSC1
somatic mutations
1. Demonstrate the feasibility of using whole genome and
capture-based sequencing methodologies in the clinical
setting to identify previously unrecognized biomarkers of
drug response in genetically heterogeneous solid tumors.
2. Hundreds of drugs have been abandoned over the years
after failing clinical trials, although many had their own
exceptional responders. Now some may be resurrected
for use (e.g. Avastin).
1. Analyzing one or a few genes in a tumor may miss
important targets for tumor treatment
Homework
Objectives
A: Become familiar with the database of the HapMap
project website
B: Learn to access the HapMap database using the
“Guide to HapMart” and HapMap Tutorial
C: Use the tools on the HapMap website to find
markers that would be useful for doing association
analysis of the RDH12 locus discussed in class.
D:
Use the browser at the www.1000genomes.org
website to find some SNPs in coding region of the
RDH12 locus
Thank you
email: [email protected]