Download The challenge: sifting through piles of variants

CSE291: Personal genomics for bioinformaticians
Class meetings: TR 3:30-4:50 MCGIL 2315
Office hours: M 3:00-5:00, W 4:00-5:00 CSE 4216
Contact: [email protected]
Today’s schedule:
• 3:30-4:10 Prioritization and Filtering
• 4:10-4:15 Break
• 4:15-4:50 Time to work on PS5 (+command line
tips and info for final presentations)
Announcements:
•
•
PS5 due Thursday
Reading posted for Thursday
Prioritizing and Filtering variants
CSE291: Personal Genomics for
Bioinformaticians
03/07/17
Outline
• The challenge: needles in
haystacks
• Annotation annotation annotation
• Family information
• Prior knowledge of gene function
• Selection
• Remaining challenges
The challenge: needles in
haystacks
The challenge: sifting through piles of variants
…
From the exome alone, THOUSANDS of candidate varian
The challenge: sifting through piles of variants
1. Have I seen
this variant
before?
6. Is the gene
likely relevant
to this
disease?
2. Is this variant in
affected family
members?
3. Does this
mutation affect
gene function?
GAAAATATCATGTGGTGTTTCC
GAAAATATCATATGGTGTTTCC
5. Gene expression
pattern of this gene
makes sense?
4. Is this position
conserved across
species?
Common approach: progressively apply filters from highest to least
confidence
Caveat: some truly pathogenic variants will fail these filters!
Annotation annotation
annotation
Annotating impact of mutations on genes
Loss of function variants
LOFTEE (https://github.com/konradjk/loftee), VEP plug-in to annotate LoF
Assesses suspected LoF variants:
• Stop gain (nonsense)
• Splice site disrupting
• Frameshift variants
KEY: filters to identify true vs. false positive LoF annotations, e.g.:
• Nonsense variants in last 5% of the gene unlikely to be that damaging
(why?)
• Nonsense variants in an exon without canonical splice sites around it
likely false positive (why?)
• Splice sites in very small introns (e.g. <15bp) likely not that critical
• If the LoF allele matches the ancestral allele, likely not really LoF (why?)
Are missense variants important?
Polyphen2: predict impact of an amino acid substitution on gene structure and
function
• 8 sequence based features, 3 structure based features
• Classify variants as: probably damaging, possibly damaging,
benign
• See also: SIFT, MutationTaster, SNAP, and more. Most
people use multiple methods and e.g. require more than one
Adzhubei et al. Nature Methods 2010
Ensemble annotations
Ensemble: combination of different methods
Idea: there’s lots of annotations out there. Some combination of which are
probably important. Let’s combine them into a single classifier
CADD: Combined Annotation-Dependent Depletion
Kircher, et al. Nature Genetics 2014 (Shendure Lab)
Features (63 annotations total):
• VEP annotations (e.g.
nonsense, missense)
• SIFT, PolyPhen2
• Mappability
• Conservation (PhastCons,
PhyloP, GERP)
• Segmental duplications
• Expression
• Histone modifications
SVM
Classifier
Train on simulated data to determine:
• Observed (likely benign) vs.
• Simulated de novos (likely pathogenic)
Family information
Pedigrees help narrow down the disease location
Thousands of candidate variants
Dozens of candidate variants
Pedigrees help narrow down the disease location
Autosomal recessive
Autosomal dominant
Bigger pedigree=better. Why?
De novo
• Causal variant
homozygous in
affecteds, missing or
heterozygous in
unaffecteds
• Affected siblings almost
always share the region
IBD=2
• Causal variant het (or
hom) in affecteds,
missing in unaffecteds
• Affected siblings likely
share the region IBD=1,
both inherited from
affected parent
• Mutation not present in
parents or affected
siblings
Prior knowledge of gene
function
Databases of clinical consequences of variants
Has my candidate gene been previously implicated in a human disease?
If yes, is it related to the current disease I’m trying to solve?
Gene ontologies
Does the annotated function of my gene
make sense?
e.g. for Marfan Syndrome, FBN1
• Biological process: skeletal/heart/kidney
development
• Cellular component: basement membrane,
extracellular, microfibril
• Molecular function: Calcium ion binding,
structural, protein binding
http://waclawikgen677s10.weebly.com/gene-
Incorporating gene expression data - tissue
Is this gene expressed in tissues that make sense for this disease?
e.g. if disease is primarily liver related, the causal gene is probably expressed in liver
We now have resources (e.g. GTeX) reporting expression across tissues
CFTR Expression by tissue
http://www.gtexportal.org/home/gene/CFTR
Incorporating gene expression data from patients
• Identified novel exon in
COL6A1 formed by
dominantly acting splice
gain event, causes external
collagen-VI-like dystrophy
• Overall, diagnosis rate of
35% in patients with
undiagnosed neuromuscular
diseases
• Can incorporate RNA-seq
from family members to
leverage traditional
pedigree approaches
Cummings et al. 2016
Selection
Types of selection
Positive selection: a new mutation confers a selective
advantage, and rises to frequency quickly. OR a new
environmental factor makes an existing mutation suddenly
more advantageous.
• Examples: LCT (lactase persistence), EDAR1
• Tests: Long haplotypes, high derived allele frequency
Purifying selection: mutations in critical regions of the
genome are often deleterious and quickly eliminated
• Examples: protein coding sequence vs. introns, ultraconserved regions
• Tests: all of these compare observed vs. expected
variation
• Tajima’s D, Fu and Li Test, many others
• Genetic constraint (Tuesday)
Selection says disease-causing mutations are rare
Effect size
severe
mild
rare
e.g. Tay-Sachs
Nonexistent
Severe Mendelian
disorders
(removed by selection)
(well actually… AD APO e4. why?)
Likely many
examples, but low
power to detect
these
e.g. high
cholesterol, Crohn’s
Disease, Type II
Diabetes
(many common alleles
with small effect sizes)
common
Allele Frequency
Metrics of purifying selection
Site frequency spectrum: the distribution of allele frequencies of a
given set of SNPs in a population or sample
Summarizing the SFS:
• % Singletons (seen 1 time in the population).
Higher=rarer=stronger selection
• Mean MAF (higher=more common=weaker selection)
• % variable sites (how many positions were never variable.
Higher=weaker selection)
Purifying selection for variant classes
MAPS: mutability adjusted % singletons
Generally, the more variants that are singletons, the more deleterious that mutation
class is
Caveat: these metrics describe categories of variants, and may or may not be useful
for predicting impact of a specific mutation
Lek et al. 2016
Conservation across species informative
• Phastcons: compute conservation based on phylogenetic tree + HMM
• phyloP: compute conservation using sequence alignment and model of neutral
evolution
• GERP: identify constrained elements in multiple sequence alignment by
quantifying “substitution deficits”
Davydov et al. 2010
Remaining challenges
Some examples defy our annotation pipelines
• Cystic fibrosis (deltaF508 in CFTR)
I
I
F G V
rs113993960 GAAAATATCATCTTTGGTGTTTCC
GAAAATATCAT---TGGTGTTTCC
I
I
In-frame deletion!
(Usually would prioritize frameshifts)
F G V
• Synonymous variants can be pathogenic! (e.g. in
MDR1, multidrug resistance. UBE1 spinal muscular
• atrophy
Deep intronic mutation
The non-coding genome…
• With exome sequencing, we analyze 2%
of the genome but still have too many
variants.
• Helped by the fact that we have a decent
idea of how to analyze coding variants.
• How will we deal with the overwhelming
number of false positives from WGS?
• Requires… annotation and prioritization!
Final projects + PS5