Class meetings: TR 3:30-4:50 MCGIL 2315 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: mgymrek@ucsd.edu 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

The challenge: needles in haystacks Annotation annotation annotation 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 variants

The challenge: sifting through piles of variants 2. Is this variant in affected family members? 1. Have I seen this variant before? 3. Does this mutation affect gene function? GAAAATATCATGTGGTGTTTCC GAAAATATCATATGGTGTTTCC 6. Is the gene likely relevant to this disease? 4. Is this position conserved across species? 5. Gene expression pattern of this gene makes sense? 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 method to call the mutation damaging 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 Causal variant homozygous in affecteds, missing or heterozygous in unaffecteds Affected siblings almost always share the region IBD=2 Autosomal recessive Causal variant het (or hom) in affecteds, missing in unaffecteds Affected siblings likely share the region IBD=1, both inherited from affected parent Autosomal dominant Bigger pedigree=better. Why? Example of different types Dominant Heterozygous De novo De novo 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-ontology.html

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, ultra-conserved regions Tests: all of these compare observed vs. expected variation Tajima’s D, Fu and Li Test, many others Genetic constraint (Tuesday) Implication: deleterious mutations are rare!

Selection says disease-causing mutations are rare severe e.g. Tay-Sachs Severe Mendelian disorders Nonexistent (removed by selection) (well actually… AD APO e4. why?) Effect size e.g. high cholesterol, Crohn’s Disease, Type II Diabetes (many common alleles with small effect sizes) Likely many examples, but low power to detect these mild rare 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 Site frequency spectrum define it Use the three different metrics: % variable sites (e.g. for de novos we’ll talk about thurs) % singleton Mean MAF 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 Missense, nonsense, syn, splice Show intron exon plot from daly Help us prioritize variation, but can only be applied to classes of vars, not single http://www.nature.com/nature/journal/v536/n7616/images_article/nature19057-f2.jpg 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 In-frame deletion! (Usually would prioritize frameshifts) rs113993960 GAAAATATCATCTTTGGTGTTTCC GAAAATATCAT---TGGTGTTTCC I I 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! More on non-coding regions next Tuesday.

Final projects + PS5