Functional analysis of DNA variants

Transcription

1 Functional analysis of DNA variants GS011143, Introduction to Bioinformatics The University of Texas GSBS program, Fall 2012 Ken Chen, Ph.D. Department of Bioinformatics and Computational Biology UT MD Anderson Cancer Center

2 Outline Lecture (1-1.5 hour) Variant annotation and functional prediction (50-60m) Break (5m) Sequence assembly (30m) Lab (30m) Variant annotation using variant effect predictor Variant annotation using Annovar NGS assembly using Velvet

3 Personalized medicine. Fernald G H et al. Bioinformatics 2011;27: The Author(s) Published by Oxford University Press.

4 Number of validated human SNPs in dbsnp overtime. Fernald G H et al. Bioinformatics 2011;27: The Author(s) Published by Oxford University Press.

5 What can we learn from variants/mutations? From individual variants Translational effect Prevalence/frequency in a population Evolutionary conservation Driver/Passenger From groups of variants (e.g. from whole genome sequencing of a population) Background mutation rate Mutation spectrum Significantly mutated genes/pathways

6 Nucleic Acids Res September; 38(16): e164 Translational effect of a variant

7 Translational effect of a variant Missense - a point mutation in which a single nucleotide is changed, resulting in a codon that codes for a different amino acid Nonsense Silent - a point mutation in a sequence of DNA that results in a premature stop codon - DNA mutations that do not result in a change to the amino acid sequence of a protein, or that do result in amino acid change but do not result in radically different properties of the changed amino acids Synonymous - A substitution in an exon that the does not alter the amino acid sequence Frameshift - A genetic mutation caused by indels (insertions or deletions) of a number of nucleotides that is not evenly divisible by three from a DNA sequence

8 DNA variants in protein coding regions Nucleic Acids Res September; 38(16): e164

9 TCGA MAF variant effect specification Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del, In_Frame_Ins, Missense_Mutation, Nonsense_Mutation, Silent, Splice_Site, Translation_Start_Site, Nonstop_Mutation, 3'UTR, 3'Flank, 5'UTR, 5'Flank, IGR, Intron, RNA, Targeted_Region De_novo_Start_InFrame, De_novo_Start_OutOfFrame

10 Nomenclature for the description of sequence variants HGVS identifier Describing genes / proteins, only official HGNC gene symbols should be used A letter indicating the type of reference sequence used - "c." for a coding DNA sequence (like c.76a>t) - "g." for a genomic sequence (like g.476a>t) - "m." for a mitochondrial sequence (like m.8993t>c) - "r." for an RNA sequence (like r.76a>u) - "p." for a protein sequence (like p.lys76asn) Reference position where the variant occurs Integers Specific changes - ">" indicates a substitution at DNA level (like c.76a>t) - "_" (underscore) indicates a range of affected residues, separating the first and last residue affected (like c.76_78delact) - "del" indicates a deletion (like c.76dela)

11 Quiz: An example HGVS identifier NOD2:NM_022162:exon4:p.R702W What does it represent? HUGO gene names NCBI Refseq database

12 RefSeq accession numbers and molecule types

13 Variant annotation software ANNOVAR Variant effect predictor

14 ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data Input Standard format of ANNOVAR input file VCF4 genotype calling format Samtools genotype-calling pileup format Complete Genomics genotype calling format GFF3-SOLiD format SOAPsnp format MAQ genotype calling format CASAVA genotype calling format Nucleic Acids Res September; 38(16): e164.

15 ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data Output Nucleic Acids Res September; 38(16): e164.

16 Variant effect predictor (VEP)

17 Variant effect predictor (VEP)

18 Case study: identifying causal genes for Mendelian diseases with recessive inheritance Exome sequencing identifies the cause of a mendelian disorder. Ng SB, Buckingham KJ, Lee C, Bigham AW, Tabor HK, Dent KM, Huff CD, Shannon PT, Jabs EW, Nickerson DA, Shendure J, Bamshad MJ Nat Genet Jan; 42(1):30-5. Published Online March Science 30 April 2010: Vol. 328 no pp DOI: /science REPORT Analysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing Jared C. Roach1,*, Gustavo Glusman1,*, Arian F. A. Smit1,*, Chad D. Huff1,2,*, Robert Hubley1, Paul T. Shannon1, Lee Rowen1, Krishna P. Pant3, Nathan Goodman1, Michael Bamshad4, Jay Shendure5, Radoje Drmanac3, Lynn B. Jorde2, Leroy Hood1,, David J. Galas1, Miller syndrome (MIM%263750)

19 ANNOVAR case study: identifying causal genes for Mendelian diseases with recessive inheritance Artificially added in NA18507 variants two known causal mutations for Miller syndrome (G->A mutation at chr16: and G->C mutation at chr16: , representing G152R and G202A in the DHODH gene) Nucleic Acids Res September; 38(16): e164.

20 Keep in mind 1. A one-variant one-phenotype model is rather unlikely 2. Variants in non-protein-coding regions are also important 3. New methods (bioinf and statistical genetics) need to be developed to address this problem

21 Functional prediction of variants What are the factors that may be useful to predict the function of a variant? Translational effect Evolutionary conservation Physico-chemical properties of protein Protein domain

22 Sorting Tolerant From Intolerant (SIFT) algorithm Nature Protocols 4, (2009)

23 Mutation Assessor Reva B et al. Nucl. Acids Res. 2011;nar.gkr407 The Author(s) Published by Oxford University Press.

24 PolyPhen-2 Eight sequence-based and three structure-based predictive features Naïve Bayes posterior probability A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging Nat Methods April; 7(4):

25 Nature Methods 7, (2010) Mutation Taster

26 Evolutionary Trace Olivier Lichtarge, BCM

27 How well do they work? Not very well One of the two DHODH variants was predicted as benign by both SIFT and PolyPhen. DHODH would have been missed, had Ng et al. utilized SIFT/PolyPhen predictions in their filtering procedure. In general, these tools lack consensus and results overlap by a small percentage (<30-40%)

28 Predicting driver/passenger mutations in cancer Driver mutation = a mutation that gives a selective advantage to a clone in its microenvironment, through either increasing its survival or reproduction. Driver mutations tend to cause clonal expansions. Passenger mutation = a mutation that has no effect on the fitness of a clone but may be associated with a clonal expansion because it occurs in the same genome with a driver mutation. This is known as a hitchhiker in evolutionary biology. CHASM Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense Mutations. Cancer Res August 15, ; CanPredict Distinguishing cancer-associated missense mutations from common polymorphisms. Cancer Research 67, CanDrA CanDrA: Cancer-Specific Driver Missense Mutation Annotation with Optimized Features

29 CHASM and SNVbox

30 Functional characterization in systems context Loss-of-function (LOF) mutations are the result of gene product having less or no function. Gain-of-function (GOF) mutations change the gene product such that it gains a new and abnormal function. These mutations usually have dominant phenotypes. Dominant negative mutations have an altered gene product that acts antagonistically to the wild-type allele. These mutations usually result in an altered molecular function (often inactive) and are characterized by a dominant or semi-dominant phenotype Lethal mutations are mutations that lead to the death of the organisms which carry the mutations. A back mutation or reversion is a point mutation that restores the original sequence and hence the original phenotype.

31 Mutation spectrum in a population Questions: which ones are GOF? which ones are LOF?

32 Mutation significance Question: among ~20,000 genes, which ones are more important for a disease? Significantly mutated genes (SMG)=Genes that are accumulated significantly more mutations than expected by chance in a population Chance? Background mutation rate Significance? Binomial test Combined P values Software CaMP MutSig MuSiC

33 A case study: Significantly mutated genes in breast and colorectal cancers

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35 Mutation spectrum

36 Mutation prevalence, drivers/passengers Over 1000 genes were somatically mutated (81% missense mutations) in 22 cell-line samples. Are they all important? Need statistical methods to estimate the probability that the number of mutations in a given gene was greater than expected from the background mutation rate. The cancer mutation prevalence (CaMP) score reflects the probability that the number of mutations actually observed in a gene is higher than that expected to be observed by chance given the background mutation rate

37 CAN-genes and their CaMP scores more in the original article CaMP scores greater than 1.0 => 122 and 69 CAN genes identified in breast and colorectal cancers

38 Each dot represents a gene Heights represent CaMP scores Mountains and hills

39 Mutation significance at gene level

40 Lawrence, TCGA symposium, 2011 Mutation rate/spectrum

41 Quiz Question: A disease has an average random background mutation rate 1e-6 per bp per patient, 3 mutations are observed in a 10,000 bp gene in a patient. Is this gene significantly mutated in this patient? Answer: In R: > 1-ppois(3, * 1e-6) [1] e-10

42 Lawrence, TCGA symposium, 2011 Rate/Spectrum cluster of human cancers

43 Lawrence, TCGA symposium, 2011 Mutation rate and replication timing

44 Mutation rate vs gene expression

45 Lawrence, TCGA symposium, 2011 Estimation of background mutation rate

46 Significantly mutated genes using gene-specific background mutation rate Lawrence, TCGA symposium, 2011

47 Significantly mutated genes across tumor types (TCGA) Lawrence, TCGA symposium, 2011

48 Significantly mutated genes in lung SQCC PS Hammerman et al. Nature 000, 1-7 (2012) doi: / nature11404

49 Most significantly mutated genes in breast cancer as determined by whole-exome sequencing (n =103) S Banerji et al. Nature 486, (2012) doi: /nature11154

50 Netbox: Automated Network Analysis Identifies Core Pathways in Glioblastoma Hypothesis: cellular networks contain functional modules, and that tumors target specific modules critical to their growth Software Netbox Hotnet Pathscan PLoS ONE 5(2): e8918

51 Mutual exclusivity of mutations in pathways Software MEMo Dendrix Genome Res February; 22(2):

52 The cbio Cancer Genomics Portal 2012 by American Association for Cancer Research Cerami E et al. Cancer Discovery 2012;2:

53 Summary Variant detection is becoming straightforward, but variant function is very difficult to predict. Ten fold of reduction in hypothesis space without losing too much sensitivity is achievable. However, the consensus among the existing tools are poor (<30-40%). Unfortunately, functional prediction is an indispensable step towards functionizing genomics due to low-throughput of functional assay, which can only afford to examine most promising hypotheses. Therefore, we need more efforts on bioinformatics/computational biology to handle things quantitatively, systematically, cost-effectively and intelligently, and more synergy between computational and experimental biologists.