Identifying genetic variants for complex disorders

Transcription

1 Identifying genetic variants for complex disorders Peristera Paschou, PhD Associate Professor of Population Genetics Dept. of Molecular Biology and Genetics Democritus University of Thrace Marianthi Georgitsi, PhD Assistant Professor of Medical Biology-Genetics Dept. of Medicine Aristotle University of Thessaloniki

2 We are quite similar, but we are different The average genome (2x3 billion bp) contains: 3-4 million single nucleotide variations, compared to the reference sequence (Single Nucleotide Polymorphisms SNPs) ~0.5 million small insertions or deletions indels (1-100bp) ~5,000 larger insertions or deletions (>100bp) Variation across all (~23,000) genes - the exome ~18,000 variants ~8-9,000 functional variants ~95% of variants are common ~ genes with new mutations ~ knock-out mutations

3 Monogenic Disorders vs Complex Disorders

4 Single gene disorders Low impact on public health cost One gene Mendelian inheritance Rare mutations Classical genetics approaches Examples: Huntington s disease Cystic fibrosis Multifactorial disorders (Complex traits) Serious impact on public health cost Multiple genes Complex pattern of inheritance Common and rare genetic variants Genome scans new technologies Examples: Stroke Diabetes Schizophrenia

5 Interaction of genetic and environmental factors Gastric ulcer Duchenne muscular dystrophy Hemophilia Diabetes Schizophrenia Bipolar disorder GENETICS ENVIRONMENT Phenylketonuria Galactosemia Cardiovascular disease Ankylosing spondylitis Car accident Rare disorders Simple genetic basis Unifactorial Common disorders Complex genetic basis Multifactorial

6 λ = Relative risk of symptom development in relatives (sibs) of affected individuals Disorder λ Schizophrenia 12 Autism spectrum disorder 150 Bipolar disorder 7 Type I diabetes 35 Crohn s disease 25 Multiple sclerosis 24 λ=1 risk in general population Nussbaum et al: Thompson and Thompson s Genetics in Medicine, 2007, p 153.

7 Gene-hunting strategies for multifactorial disorders Candidate gene approach Disorder Etiological pathway Gene Mapping Positional cloning Disorder Mapping Gene Etiological pathway

8 Gene identification -Size of the human genome 3 x 10 9 bp -23,000 genes -Large portion of the genome function is not known. Claude Maunet Haystack at the Sunset near Giverny

9 Looking for genes in 23 pairs of chromosomes

10 SNPs can be used to create dense marker maps microsatellites SNPs Recent genome-wide association studies use millions of SNPs.

11 Positional cloning Clinical Phenotype Indications for a genetic basis? Study design families Sib pairs Single patients Sample and data collection Analysis Linkage analysis Association studies Define candidate regions Physical mapping/ gene identification

12 Linkage analysis Large families Sib-pair studies

13 1989 The cystic fibrosis gene is identified First successful positional cloning study

14 Linkage analysis What is the probability that the disease-causing mutation and the polymorphism we are studying are linked (at a specific genetic distance) vs non-linked?

15 Recombination and linkage Two loci are linked when little or no recombination occurs among them. Recombination: Exchange of DNA segments among homologous regions during meiosis and gamete formation. gametes

16 Recombination and linkage Two loci at different chromosomes cannot be linked. Linkage increases as physical distance decreases. 1cM distance among two genetic loci, means that 1% of produced gametes will be recombined.

17 Logarithm of the odds LOD score Logarithm of the probability of linkage Ratio of the probability that the two loci are linked vs the probability that they are not linked. likelihood of obtaining the test data if the two loci are indeed linked, to the likelihood of observing the same data purely by chance. LOD = 3.0 = odds of 1000/1 for linkage Corresponds to error of 5%

18 Linkage analysis Great power for gene identification for mendelian disorders Limited success when used for multifactorial disorders 1,2 1,2 1,2 1,2 1,1 1,1 2,2 1,1 2,2 2,2 1,1 2,2

19 Association studies Population studies trios

20 Genetic Association Studies Aim: To unravel associations between genetic data (ie alleles or genotypes) of commonly occurring genetic variants with information regarding a trait or a medical phenotype (ie disease) under study, using statistical analyses and a large enough sample size (typically cases versus controls), in order to support the statistics that these variants contribute to trait/disease risk. Examples of complex diseases Type II Diabetes Mellitus Obesity Cardiovascular diseases Cancer (non-hereditary) Osteoarthritis Autoimmune disorders Alzheimer s disease. Schizophrenia Autism Bipolar Disorder Obsessive Compulsive Disorder Learning disabilities (Dyslexia). 13/04/2016

21 Linkage disequilibrium (LD) The non random association of alleles at different loci Essential tool for genetic association studies

22 Example Linkage Disequilibrium SNP1: A 50% C 50% SNP2: A 50% G 50% SNP1 SNP2 expected frequency of haplotypes A A 0,5 x 0,5 A G 0,5 x 0,5 C A 0,5 x 0,5 C G 0,5 x 0,5 If total LD exists only 2 haplotypes will be observed (eg) A G C A

23 LD around a mutation generations k g

24 Extant chromosomes mutation Ancestral chromosome Ancestral DNA sequence Novel DNA sequence due to recombination Jobling, Hurles & Tyler-Smith. Human Evolutionary Genetics

25 Extant chromosomes mutation Ancestral chromosome Ancestral DNA sequence Novel DNA sequence due to recombination

26 Extant chromosomes polymorphism mutation Ancestral chromosome Ancestral DNA sequence Novel DNA sequence due to recombination

27 Extant chromosomes mutation SNPs Ancestral chromosome Ancestral DNA sequence Novel DNA sequence due to recombination

28 Genome-wide Association Studies (GWAS) Ideally Identify all SNPs (eg ) Collect a very large sample (eg 1,000 patients and 1,000 control individuals) Genotype all individuals for all SNPs 30 billion genotypes! Cost in 2002: 50 cents/genotype. $15 billion for each disorder!

29 Genome structure allows the selection of tagging SNPs mutation SNP mutation Hirschhorn & Daly, Nat Rev Genet 2005 Candidate gene or GWAS signal SNPs suffice for a rough scan of the human genome Paschou et al. Genome Research 2007 De Bakker et al. Nature Genetics 2005

30 Genetic Association Studies versus Linkage studies Appropriate for complex phenotypes Increased genetic (locus) heterogeneity (ie multigenic variance) many genes, many variants Common variance ( common disease common variants concept) modest disease risk per variant Inadequate power to detect associations Large numbers of cases and controls (healthy individuals), or family-based associations (trios, ie affected child and both parents) or extreme phenotypes Mostly SNPs (taggingsnps): A single or a few SNPs within a chromosomal region that capture(s) (ie tags ) most of the common DNA variation in this particular region, owing to the effect of Linkage Disequilibrium (LD). Associated SNPs most often not coding (they can be in LD with the causal variant or have a regulatory effect) Appropriate for Mendelian traits Reduced genetic heterogeneity (one or a few genes) Typically rare variants ( rare disease rare variants concept) in all affected individuals Large, multigenerational pedigrees detection power (parametric, non-parametric linkage analyses) Typically microsatellite markers, SNPs, or long stretches of chromosomal homozygosity (for recessive traits only) Note: LD is defined as the phenomenon of coinheritance (non-random association) of studied genetic marker (SNPs) alleles, unlikely to be separated by homologous recombination ( linked markers) within a population. 13/04/

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32 Technology reduces the cost 2007 Candidate genes 1$ per SNP SNPs

33 Technology reduces the cost 2007 Candidate genes 1$ per SNP SNPs Genome genotyping (GWAS) 250$ 1 million SNPs

34 Genome-wide association studies (GWAS) Patients Thousands of samples The more the better! Controls Genotyping eg 600,000 SNPs Comparison of alleles Statistical analysis

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36 September 2012: studies associated SNPs

37 Current status of available SNP genotyping platforms A comparison 13/04/2016

38 Workflow of a typical GWAS study Sampl e collecti on Peripheral blood Buccal cells DNA extraction and Quantifica tion Manual purification Kit-based purification DNA Plating 96-well plates 384-well plates Array run QC Pathwa y analyse s Statistica l analysis (traitdependent) Replication study (stage 2) ANOVA Chi-square Fisher s Exact Covariate adjustment Multiple testing Sample size Population Phenotypic criteria Fluorospectrophotometry Spectrophotometry Metaanalyse s Validation (technical, functional) Other genotyping platform In silico analysis (SNPs in LD) Gene expression analysis Cell-based analysis Animal models Resequencing of region Marianthi Georgitsi 13/04/2016

39 T2DM associated genes before 2007 Confirmed susceptibility genes in the pre-gwas era Altshuler et al, Nature Genetics 2000 Gloyn et al, Diabetes 2003 Grant et al, Nature Genetics 2006

40 T2DM associated genes after 2007 Candidate gene studies GWAS for T2DM GWAS for DM associated traits Meta-analysis studies

41 Body Mass Index GWAS and meta-analysis women of African ancestry >3.2 million SNPs 39,144 men and women of African ancestry Monda et al. Nature Genetics 2013

42 Alleles associated with multifactorial disorders differ in frequency around the world CORONA ET AL. 12TH INTERNATIONAL CONGRESS OF HUMAN GENETICS, 2011

43 Different genetic structure in different populations Significant differentiation even among European populations SNPs in different European populations. Adygei Sardinian Russian Italian/Tuscan Orcadian French Basque Paschou et al. Journal of Medical Genetics 2010 Paschou et al. PLoS Genetics 2008 Paschou et al. PLoS Genetics 2007

44 For each population, the average individual as well as standard deviation is shown Stathias et al. Annals of Human Genetics 2012 Principal Components Analysis 1,200 SNPs CEU Greeks TSI (Italians) (North Europe) (South Europe)

45 Population 1 cases Population 2 Population stratification is a confounding factor in genetic association studies controls aa Aa AA

46 Using genetic markers to correct for population stratification in genome-wide association studies European Asian African Paschou et al PLoS Genet 2007 Paschou et al PLoS Genet 2008

47 How many more genes are we looking for? Type 2 Diabetes already 45 genes associated DIAbetes Genetics Replication And Metaanalysis (DIAGRAM) Consortium 34,840 patients 114,981 controls 10 additional genes Morris et al. Nature Genetics 2012

48 Type 2 Diabetes associated genes account for 10-28% of heritability

49 The case of the missing heritability (Maher, Nature 2008)

50 What GWAS studies have to offer Pros and Cons Pros (+) High-throughput analysis (hundreds of thousands up to million variants) Large-scale projects with thousands participants Suitable for complex, non-mendelian disorders, quantitative traits, eqtls, Variety of software tools for computerized analysis Dataset repositories for meta-analyses continuously curated and updated Pathway analyses Certified service providers worldwide... Missing heritability of common diseases/disorders Marianthi Georgitsi Cons (-) They target pre-defined variants (biased) Not suitable for identification (de novo) studies Not suitable for studying Mendelian disorders Alleles confer small effect sizes [small Odds Ratios (typically OR<1.5)] False-positive (population stratification, genotyping errors, selection bias, etc) or falsenegative results (insensitivity to rare variants, lack of genetic variants from platforms, lack of variation in a SNP in the population under study) The richer they are in context, the more expensive Their analysis requires training in bioinformatics and computationally intensive analyses and infrastructure Functional approaches to interpret the data are needed (gene expression analysis, cell and animal model manipulations, etc)...

51 What GWAS studies have to offer Prof. Scharf J, Massachusetts General Hospital, Boston, personal communication, 2015 Missing heritability not targeted by GWAS studies Rare variants (MAF<1%) Structural variants Gene-gene interactions Gene-environment interactions Population isolates and population extremes Human Genome Low-hanging fruit? (high penetrance, high frequency) Marianthi Georgitsi

52 Where is the missing heritability? Common disease rare variant hypothesis McCarthy et al. Nat Rev Genet 2008

53 Technology reduces the cost 2007 Candidate genes 1$ per SNP SNPs Genome genotyping (GWAS) 250$ 1 million SNPs

54 Technology reduces the cost ? Candidate genes Genome genotyping (GWAS) Exome sequencing (NGS) Whole genome sequencing (NGS) 1$ per SNP 250$ 1000$ < 1000$ SNPs 1 million SNPs All exons The whole genome

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56 1000 genomes project

57 Nature 526, (01 October 2015)

58 Exome sequencing identifies the cause of a Mendelian disorder Four affected individuals in three independent kindreds, Exome sequencing at 40x coverage. Filtering against public SNP databases and eight HapMap exomes identified a single candidate gene DHODH (Dihydroorotate dehydrogenase), encodes a key enzyme in the pyrimidine de novo biosynthesis pathway. Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome. Ng et al. Nature Genetics 2010

59 Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome Ten unrelated individuals. Seven individuals had nonsense or frameshift mutations at MLL2 MLL2 - encodes a Trithorax-group histone methyltransferase Sanger sequencing revealed MLL2 mutations in 2 of the remaining individuals De novo or transmitted according to phenotype Ng et al. Nature Genetics 2010

60 Exome sequencing to identify rare variants in complex disorders? Large families unrelateds trios Comparing the extremes of a phenotypic distribution Bamshad et al. Nature Reviews Genetics, 2011

61 The Exome Sequencing Project Sequencing the exomes of >7,000 individuals with extreme phenotypes Goal: find genes that underlie common cardiovascular disease (eg earlyonset myocardial infarction and stroke) and lung disease (eg chronic obstructive pulmonary disease) Funded by the US National Heart, Lung, and Blood Institute Fu et al. Nature 2013 Boileau et al. Nature Genetics 2012 Emond et al. Nature Genetics 2012 Tennessen et al. Science 2012

62 Data analysis of a typical NGS experiment Illumina site:

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65 Workflow to identify disease variants in exome sequencing data Pabinger et al. Briefings in Bioinformatics, 2013

66 Making sense of NGS data Factors affecting the identification of causal alleles against a background of common polymorphisms: ~ SNPs (exome-seq) ~2% of SNPs identified per individual by WES is novel ~ SNPs (exome-seq) What is the mode of inheritance of a trait? Does population structure affect causal alleles? Does the phenotype arise de novo or due to inherited variants? Locus heterogeneity of a trait (=how many genes affect the manifestation of a trait) How large sample size in order to identify traitassociated alleles? What analytical framework (software, pipeline) to be used? Marianthi Georgitsi

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68 20,000-25,000 exonic variants What is the effect? Coding? Intronic? Deleterious? Non-functional? Amino-acid change? Regulatory?

69 Who else has the variant? Human Variation Databases Goal: Determine presence of variant in others dbsnp - Includes everything!! - SNPs have information about origin -VCF available ClinSeq Exomes with phen. (dbgap) - CCDS and knowngenes - soon in dbsnp, VCF to be available 1000 Genomes - 1,094 low coverage genomes (~4x) - >=1% sensitivity - In dbsnp, VCF available Exomes CCDS (coming soon) NHLBI Exome Sequencing Project - 2,500 Exomes with phen. (dbgap) - in dbsnp, VCF available

70 Potential frequencies of causal variants in complex traits Cirulli and Goldstein, Nature Reviews Genetics 2010

71 How NGS has advanced our knowledge in disease predisposition and disease gene identification Chong et al., AJHG, 97(2): , 2015

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73 Risk of developing a disease Lifestyle changes Personalized treatment (pharmacogenomics) Less toxicity & ADR / Better response Family planning Prenatal screening / Genetic counseling Presymptomatic or predictive screening Disease management and follow-up

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