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MUS2046 Genetics in Medicine Finding disease genes Cathryn Lewis Professor of Genetic Epidemiology and Statistics.

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Presentation on theme: "MUS2046 Genetics in Medicine Finding disease genes Cathryn Lewis Professor of Genetic Epidemiology and Statistics."— Presentation transcript:

1 MUS2046 Genetics in Medicine Finding disease genes Cathryn Lewis Professor of Genetic Epidemiology and Statistics

2 Introduction to genetics: 1 DNA structure

3 Introduction to genetics: 2 DNA sequence

4 What makes us different? These differences control our hair colour, our height, and the diseases we will get Introduction to genetics: 3 DNA differences

5 Complex disease: contributions from genetic and environmental factors Gene4 Env4 Env3 Env2 Env4 Disease Env1 Gene8 Gene7 Gene6 Gene5 Gene4 Gene3 Gene2 Gene1 Examples: asthma, breast cancer, heart disease, autism, arthritis, migraine, obesity, diabetes, stroke Most diseases that have a major economic, social and health burden

6 Complex Diseases Raised risk in families – But increase in risk may be slight compared with population risk – Can be measured by the sibling relative risk No clear mode of inheritance Multiple genes Environmental effects Gene-gene, gene-environment interactions Examples Inflammatory bowel disease, multiple sclerosis, depression, asthma, rheumatoid arthritis, diabetes, heart disease,.... Most diseases that have a considerable public health impact

7 Genetic Association Study A genetic association study tests whether the presence of a specific genetic variant correlates with a trait of interest (such as risk of disease) A SNP has two alleles: C, T Each individual has a genotype at this SNP –CC, CT or TT

8 Genetic variation: Single nucleotide polymorphism (SNP) Alleles A and C are present in the population Genotype : carried by an individual, on paternal and maternal inherited chromosomes....TGGACCTGCA TGGACATGCA TGGACCTGCA TGGACATGCA TGGACATGCA TGGACCTGCA.... Genotype: AA ACCC

9 Genetic Association Study A genetic association study tests whether the presence of a specific genetic variant correlates with a trait of interest (e.g. presence/absence of disease)

10 Identifying SNPs that increase risk of disease Cases – affected with disease Controls – not affected with disease Genotype SNP with A, C alleles: AAACCC More AC and CC genotypes in cases than in controls Indicates that carrying C allele increases risk of disease

11 Case control studies Compare frequency of SNP alleles or genotypes in a series of cases and controls Cases – Diagnosed with disease – Ascertainment - through hospital or community? – Define criteria for inclusion in study Controls – Unaffected with disease (supernormal controls) – Randomly ascertained (e.g. blood donors) – Both types of controls are valid Important to match cases and controls on genetic ancestry – if not, genetic differences between cases and controls may reflect their ancestry, not their disease status

12 Association of PTPN22 mutation with rheumatoid arthritis (RA) Steer et al., Arthritis Rheum, 2005 RA is a complex disease with a sibling relative risk of approximately 3, and a strong HLA effect PTPN22 encodes a protein tyrosine phosphatase which interacts with the negative regulatory kinase Csk to inhibit T cell signalling and activation The R620W mutation was shown in other studies to increase risk of RA Association study of R620W performed in London RA patients 302 RA cases (hospital-ascertained) and 374 controls, all of European ancestry

13 Association of PTPN22 mutation with RA Significant difference in allele frequency (p=3 x ) 17.2 ( ) 1.7 (1.2 –2.5) 1Odds ratio (95% CI) 15.9%12 4% 72 24% % Cases (n=302) 8.4%1 0.3% 61 16% % Controls (n=374) Freq. of T allele TTCTCC Odds ratio of CT genotype compared to CC genotype = 312 x 72 / (218 x 61)

14 Association of PTPN22 mutation with RA 17.2 ( ) 2.05 ( ) 1Allelic Odds ratio (95% CI) 15.9%12 4% 96 16% % Cases (n=302) 8.4%1 0.3% 63 8% % Controls (n=374) Freq. of T alleleTTTC Odds ratios for genotypes CC, CT, TT are 1, r, r 2 Here, OR for CC 1 (baseline), OR for CT=2.05, OR for TT = 4.02

15 Rheumatoid arthritis: contributions from genetic and environmental factors STAT4 TRAF1 PTPN22 Gene4 ? Sex Age Env4 Rheumatoid arthritis Smoking HLA TNFAIP3 CD40 CTLA4 Other genes Now over 100 genes identified that are associated with rheumatoid arthrtis

16 Genome-wide association studies (GWAS) SNP chips from Illumina and Affymetrix will genotype up to 1 million SNPs across the genome Capture most of the variation across the genome

17 WTCCC (2007) Nature 447:

18 Steps in WGA study Design study Collect samples Define phenotypes Type DNA on whole-genome panel Quality Control (QC) SNP-by-SNP analysis Interpret results Replicate, perform meta-analysis

19 GWAS analysis methods SNP-by-SNP analysis against phenotype – Analysis of genotype counts – Regression analysis of quantitative trait – Logistic regression of case-control status on SNP genotype, ancestry covariates, phenotypic covariates, environmental factors.... – Problems of multiple testing with 500K SNPs

20 25000 p-values<0.05 Multiple Testing Approaches How to make sense of ½ million p-values?

21 QQ (quintile-quintile) plot Expected log(p-values), ordered Observed log(p-values) ordered Elevation above line implies observed results more significant than expected: true signal or artefact?

22 Manhattan plot -log(pvalue) of each SNP plotted. Thresholds at genome-wide significance (5 x ) and suggestive significance (5 x )

23 SNP rs (lies near gene TNFSF18) Risk allele, T, has frequency Odds ratio for this allele is 1.22

24 SNP risk information: Crohn’s disease SNP name : rs – Gives information on location, which gene(s) the SNP is in (or near) Allele frequency: allele T has frequency – What are the frequency of the two alleles? – Can use to calculate genotype frequencies Odds ratio: 1.22 for allele T – How much does carrying the ‘risk’ allele increase your risk of disease? GenotypeFrequencyOdds ratio Underlying model for odds ratio CC (baseline) CT r TT r2r2

25 Regional association plot of association with SNPs on chr 19 associated with LDL cholesterol levels Strongest association with rs Other SNPs also have significant evidence of association SNPs are highly correlated (red), so picking up same information Which is relevant genes? The most stronly associated SNPs do not lie in the gene SNPs probably affect regulation of LDLR gene (strong functional candidate gene)

26 Mendelian disorderComplex disease

27

28 How can we use genetics in a clinical setting? Disease risk estimation Can we identify individuals at high risk of disease? – offer appropriate screening protocols for early diagnosis – ‘healthy’ living, reducing the environmental risk component – preventative therapy? Diagnosis and prognosis – Using genetics to help in diagnosis, avoiding expensive clinical tests – Predict future disease path and treat accordingly Personalised medicine: pharmacogenetics and therapeutics – Using genetic profiles to identify the most effective drug or therapy – Avoid drugs likely to have major side-effects Gain insight into disease pathways from knowledge of gene function – Deeper understanding of disease mechanism and prevention – New targets for drug development

29 Can we use risk SNPs to identify individuals at high risk of disease? Risk prediction 1. Theory Using breast cancer risk SNPs to estimate the distribution of risks in the population 2. Practice Using a cohort study to assess the predictive ability of T2D SNPs

30 Disease prediction from genetic association studies Test 500k SNPs across genome for differences between cases and controls Identify panels of SNPs that control risk of disease Each SNP: odds ratio of disease, frequency in population For any individual, can calculate genetic risk profile across these SNPs

31 Multiply odds ratios from each gene to give overall Relative risk = 1.30 (slight increase in risk, compared to average risk of 1) Can we use the low risk genes to predict a woman’s risk of breast cancer? Gene4 Breast cancer Gene8 Gene7 Gene6 Gene5 MAP3K1 ZNF365 TOX3 FGFR ………

32 Distribution of genetic risk in the population Increased risk: Carry many risk alleles Decreased risk: Carry few risk alleles How useful is this information for: Screening? Therapeutic interventions? Lifestyle management? 5% and 1% of population at highest risk Baseline risk

33 Can we use risk SNPs to identify individuals at high risk of disease? Risk prediction 1. Theory Using breast cancer risk SNPs to estimate the distribution of risks in the population 2. Practice Using a cohort study to assess the predictive ability of T2D SNPs

34 How do genes and environmental/clinical risk factors help predict individuals who develop type 2 diabetes? Non-genetic risk factors: Framingham risk scores – Age, BMI, cholesterol, family history, blood pressure, fasting glucose Genetic risk factors: 20 SNPs 5535 healthy individuals: 303 developed T2D over next 10 years

35 Fig 2 Percentage of participants in each gene count score category among those who developed type 2 diabetes and those who remained free from diabetes. Talmud P J et al. BMJ 2010;340:bmj.b4838 ©2010 by British Medical Journal Publishing Group

36 Fig 1 Receiver operating characteristics curves for gene count score alone (area under curve 0.54, 95% CI 0.50 to 0.58), Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82), and gene count score incorporated into Framingham offspring risk score (area under curve 0.78, 0.75 to 0.81). Talmud P J et al. BMJ 2010;340:bmj.b4838 ©2010 by British Medical Journal Publishing Group

37 Finding genes for complex disorders – how are we doing? Identified SNPs only account for a small proportion of the genetic contribution to disease Disease Number of loci Proportion of heritability explained Heritability measure Age-related macular degeneration550%Sibling recurrence risk Crohn's disease3220%Genetic risk (liability) Systemic lupus erythematosus615%Sibling recurrence risk Type 2 diabetes186%Sibling recurrence risk HDL cholesterol75.2% Residual phenotypic variance Height405%Phenotypic variance Manolio et al., Nat Genet, 2009

38 How do we find the missing heritability? Genotype denser SNPs genome-wide Identify the causal variant, (not necessarily the SNP on the GWAS chip) Account for gene-gene, gene-environment interactions Epigenetics? Systems biology approach: genotype, gene expression, proteomics, epigenetics, environment, clinical data

39 DIRECT-TO-CONSUMER GENETIC TESTING

40

41 23andme.com

42 In November 2013, the US FDA banned 23andme from giving information about disease risks Only give information on ancestry currently

43 What did 23andme test for? Carrier Status (53) ARSACS Agenesis of the Corpus Callosum with Peripheral Neuropathy (ACCPN) Alpha-1 Antitrypsin Deficiency Autosomal Recessive Polycystic Kidney Disease BRCA Cancer Mutations (Selected) Beta Thalassemia Bloom's Syndrome Canavan Disease Congenital Disorder of Glycosylation Type 1a (PMM2-CDG) Connexin 26-Related Sensorineural Hearing Loss Cystic Fibrosis D-Bifunctional Protein Deficiency DPD Deficiency Dihydrolipoamide Dehydrogenase Deficiency Factor XI Deficiency Familial Dysautonomia Familial Hypercholesterolemia Type B Familial Hyperinsulinism (ABCC8-related) Familial Mediterranean Fever Fanconi Anemia (FANCC- related) G6PD Deficiency GRACILE Syndrome Gaucher Disease Glycogen Storage Disease Type 1a Glycogen Storage Disease Type 1b Hemochromatosis (HFE-related) Hereditary Fructose Intolerance Hypertrophic Cardiomyopathy (MYBPC3 25bp-deletion) LAMB3-related Junctional Epidermolysis Bullosa Leigh Syndrome, French Canadian Type (LSFC) Limb-girdle Muscular Dystrophy Maple Syrup Urine Disease Type 1B Medium-Chain Acyl-CoA Dehydrogenase (MCAD) Deficiency Mucolipidosis IV Disease risk(122) Abdominal Aortic Aneurysm Age-related Macular Degeneration Alcohol Dependence Alopecia Areata Alzheimer's Disease Alzheimer's Disease: Preliminary Research Ankylosing Spondylitis Asthma Atopic Dermatitis Atrial Fibrillation Atrial Fibrillation: Preliminary Research Attention-Deficit Hyperactivity Disorder Back Pain Basal Cell Carcinoma Behçet's Disease Bipolar Disorder Bipolar Disorder: Preliminary Research Bladder Cancer Brain Aneurysm Breast Cancer Breast Cancer Risk Modifiers Celiac Disease Celiac Disease: Preliminary Research Chronic Kidney Disease Traits (60) Adiponectin Levels Alcohol Flush Reaction Asparagus Metabolite Detection Avoidance of Errors Biological Aging Birth Weight Bitter Taste Perception Blood Glucose Breast Morphology Breastfeeding and IQ C-reactive Protein Level Caffeine Consumption Childhood and Adolescent Growth Chronic Hepatitis B Earwax Type Eating Behavior Eye Color Eye Color: Preliminary Research Finger Length Ratio Food Preference Freckling HDL ("Good") Cholesterol Levels HIV Progression Hair Color Hair Curl Hair Curl: Preliminary Research Hair Thickness Height Hypospadias Iris Patterns LDL ("Bad") Cholesterol Levels Drug Response (24) Abacavir Hypersensitivity Alcohol Consumption, Smoking and Risk of Esophageal Cancer Antidepressant Response Beta-Blocker Response Caffeine Metabolism Clopidogrel (Plavix®) Efficacy Floxacillin Toxicity Fluorouracil Toxicity Hepatitis C Treatment Side Effects Heroin Addiction Lumiracoxib (Prexige®) Side Effects Metformin Response Naltrexone Treatment Response Oral Contraceptives, Hormone Replacement Therapy and Risk of Venous Thromboembolism Phenytoin (Dilantin®) Sensitivity (Epilepsy Drug) Postoperative Nausea and Vomiting (PONV) Pseudocholinesterase Deficiency Response to Hepatitis C Treatment Response to Interferon Beta Therapy Statin Response Sulfonylurea Drug Clearance (Type 2 Diabetes Treatment) Thiopurine Methyltransferase Deficiency Warfarin (Coumadin®) Sensitivity

44 Absolute risk Relative risk

45 Traits

46 Parkinson’s, Alzheimer’s: Locked

47 23andme: psoriasis

48

49 Psoriasis Type 1 diabetes GeneSNPMy geno- type Adjusted OR HLA-C rs CT1.83 IL12B rs TT1.16 IL23R rs GG1.06

50 “ …. better off spending their money on a gym membership or personal trainer” Hunter, Khoury & Drazen, N Engl J Med, 2008

51 RBI: Risk.... burden... intervention Is the risk conferred large? Is the disorder or trait severe? Is there anything we can do about it? Is it really worth worrying about a relative risk of 1.05? Schizophrenia? Baldness? High blood pressure? Breast cancer? Prevention? Early diagnosis?

52 Genetic risk profile Case studies of three common adult-onset disorders : – Coronary artery disease – Colorectal cancer – Type 2 diabetes Do SNPs enable us to identify individuals at high risk of disease? How do risk from SNPs compare to other known risks of disease? Method: Identified SNPs most strongly associated with disease and calculated risk profiles expected in the population (‘theoretical distribution’) Compared to calculations of other risk factors from the literature

53 Odds ratios: Comparing genetic and other risk factors Family history, other risk factors DiseaseOR for highest 5% of population Family history (affected sibling) Epidemiological & risk factors Coronary artery disease Total cholesterol Smoking Colorectal cancer1.65.1Smoking Obesity Type 2 diabetes1.73.5Obesity2.5 Environmental risk factors and family history are more predictive than genetics (currently)

54 Empowerment or endangerment?

55 US family history tool https://familyhistory.hhs.gov/fhh-web/home.action

56 Summary Scientific strides in identifying the inherited genetic variants that affect disease risk Gives biological insights into the disease Very limited disease prediction available from current findings – Incomplete knowledge of polygenic component of disease – Causal genetic variants are unknown Better prediction comes from – Family history – Environmental risk factors (smoking, body mass index) – Pre-clinical factors (blood pressure, cholesterol levels)


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