QTL Studies- Past, Present and Future

1. QTL Studies- Past, Present and Future David Evans

2. Genetic studies of complex diseases have not met anticipated success Glazier et al, Science (2002) 298:2345-2349

3. Korstanje & Pagan (2002) Nat Genet Korstanje & Pagan (2002) Nat Genet

4. Linkage Marker Gene1 Linkage disequilibrium Mode of inheritance Linkage Association Gene2 Complex Phenotype Individual environment Gene3 Common environment Polygenic background Reasons for Failure? BUT…Not much success in mapping complex diseases / traits ! Weiss & Terwilliger (2000) Nat Genet

5. LD Patterns and Allelic Association Type 1 diabetes and Insulin VNTR Alzheimers and ApoE4 Bennett & Todd, Ann Rev Genet, 1996 Pattern of LD unpredictable Roses, Nature 2000 Extent of common genetic variation unknown

6. Genome-wide Association? Risch & Merikangas, Science 1996

7. Multiple Rare Variant Hypothesis? GWA assumes that common variants underlie common diseases

8. Enabling Genome-wide Association Studies HAPlotype MAP High throughput genotyping Large cohorts “ALSPAC”

9. Wellcome Trust Case Control Consortium

10. Wellcome Trust Case-Control Consortium Genome-Wide Association Across Major Human Diseases • CASES • Type 1 Diabetes • Type 2 Diabetes • Crohn’s Disease • Coronary Heart Disease • Hypertension • Bipolar Disorder • Rheumatoid Arthritis • Malaria • Tuberculosis • Ankylosing Spondylitis • Grave’s Disease • Breast Cancer • Multiple Sclerosis • CONTROLS • 1. UK Controls A (1,500 - 1958 BC) • 2. UK Controls B (1,500 - NBS) • 3. Gambian controls (2000) DESIGN Collaboration amongst 26 UK disease investigators 2000 cases each from 9 diseases 1000 cases from 4 diseases GENOTYPING Affymetrix 500k SNPs Illumina Human NS_12 SNP chip

12. Ankylosing Spondylitis Auto-immune arthritis resulting in fusion of vertebrae Prevalence of 0.4% in Caucasians. More common in men. Often associated with psoriasis, IBD and uveitis Ed Sullivan, Mike Atherton

13. IL23-R ARTS-1 Ankylosing Spondylitis GWAS WTCCC (2007) Nat Genet

14. 8q24 ARTS 6q251 FTO 2q36 INS 18p11 12q24 8q24 2q35 LSP1 IL2RA FTO 8q24 Successes… MSMB Breast ca Prostate ca Colorectal ca Crohn’s Dis IBD T1D T2D Obesity Triglycerides CAD Coeliac Dis Rheumatoid A Ankylosing S Alzheimer Dis Glaucoma Asthma 10q21 TCF2 Common genes = common etiology? JAZF1 PTPN2 WFS1 IFIH1 Some in gene deserts CTBP2 KCNJ11 FAM92B CTLA4 Large relative risks does not = success HNF1B NKX2-3 TCF7L2 ERBB3 11q13 Drug targets NCF4 CD25 SLC30A8 3des. PTPN22 PHOX2B PPARG KIAAA035D NUDT11 IRGM HHEX SLC22A3 BSN IGF2BP2 MAPKI3 KLK3 ATG16L1 TNRC9 CDKAL1 IL21 5p13 IL23R LMTK2 SMAD7 9p21 FGFR2 NOD2 IL2 PTPN22 GALP LOXL1 ORMDL IL23R GCKR 9p21

15. 1 Gene  3 Genotypes  3 Phenotypes 2 Genes  9 Genotypes  5 Phenotypes 3 Genes  27 Genotypes  7 Phenotypes 4 Genes  81 Genotypes  9 Phenotypes What About Quantitative Traits? Central Limit Theorem  Normal Distribution Quantitative genetics theory suggests that quantitative traits are the result of many variants of small effect Unselected samples The corollary is that very large sample sizes will be needed to detect these variants in UNSELECTED samples

17. But, no signal in an American T2D scan…? FTO produces a moderate signal in WTCCC T2D scan FTSO WTCCC T2D Scan FTO -American cases and controls matched on BMI

18. Replication is critical !!! FTO

19. Height- The Archetypal Polygenic Trait Dizygotic Twins Twin, family and adoption studies suggest that, within a population, 90% of variation in height is due to genetic variation Monozygotic Twins Twins separated at birth Borjeson, Acta Paed, 1976

20. Large numbers are needed to detect QTLs !!! Collaboration is the name of the game !!! GWA of Height A- 1914 Cases (WTCCC T2D) B- 4892 Cases (DGI) C- 6788 Cases (WTCCC HT) D- 8668 Cases (WTCCC CAD) E- 12228 Cases (EPIC) F- 13665 Cases (WTCCC UKBS) Significant results Weedon et al. (in press) Nat Genet Other loci?

21. Some real hits sit in the bottom of the distribution Some hits initially look interesting but then go away A: 1,900 B: 5,000 C: 7,200 D: 9,100 E: 12,600 F: 14,000 Weedon et al. (in press) Nat Genet

22. Hedgehog signaling, cell cycle, and extra-cellular matrix genes over-represented Weedon et al. (in press) Nat Genet

23. The combined impact of the 20 SNPS with a P < 5 x 10-7 • The 20 SNPs explain only ~3% of the variation of height • Lots more genes to find – but extremely large numbers needed Weedon et al. (in press) Nat Genet

24. Height Linkage Regions Perola et al, Plos Genetics, 2007; data available at http://www.genomeutwin.org; Weedon et al.; unpublished data

25. Perola et al, Plos Genetics, 2007; data available at http://www.genomeutwin.org; Weedon et al.; unpublished data

26. What’s Going On? Loci identified by GWAs don’t have linkage peaks over them Linkage analysis lacks power? Areas identified by linkage don’t have significant assocation hits over them Type I error? Power? BUT…what if linkage analysis and association analysis identify different types of loci?

27. What next? Genome-wide Sequencing Functional Studies Animal models Transcriptomics Other ethnic groups Initial Genome Wide Scans Mendelian Randomisation Epigenetics Genomic Profiling Fine mapping CNVs More genes

28. Distribution of MAFs in HapMap Genome-wide panels and HapMap biased towards common variants Common variants don’t tag rare variants well

29. Complex Disease Tree TCF7L2 SEMA5A TCF2 ??? WFS1 FTO ??? ??? ??? PPARG ??? ??? “High hanging fruit” ARTS1 “Low hanging fruit” IL23R HLA-B27 “Rotten Fruit”

30. Methods of gene hunting rare, monogenic (linkage) Effect Size common, complex (association) ? Frequency

31. Genome-wide Sequencing Sequence individuals’ genomes Will identify rare variants But will we have enough power? Evans et al. (2008) EJHG

32. Genomic Profiling The idea of using genetic information to inform diagnosis Predictive testing in the case of monogenic diseases has been used for years (1300+ tests available) (e.g. Phenylketonuria) Not possible in complex diseases as effects of an individual variant is so small BUT…if we consider several predisposing genetic and environmental factors, can we predict disease?

33. Genomic Profiling (from Janssens et al. 2004 AJHG) => Give up and go home? (from Yang et al. 2003 AJHG)

34. Ankylosing Spondylitis Prevalence of B27+, ARTS1+,IL23R+ is 2.4% Prevalence of B27-, ARTS1-, IL23R- is 19% (Brown & Evans, in prep)

35. Using Genetics to Inform Classical Epidemiology

36. Observational Studies Fanciful claims often made from observational studies In a case-control study, a group of diseased individuals are recruited (Cases); A group of individuals without disease are gathered (Controls); Both groups are then measured retrospectively on an exposure of interest; A test of association is performed Example: Obesity (Exposure) and Coronary Heart Disease (Outcome) Odds of obesity in cases: 200/100 = 2 Odds of obesity in controls: 50/250 = 0.2 Odds Ratio: 2/0.2 = 10

37. Classic limitations to “observational” science • Confounding • Reverse Causation • Bias

38. Randomization controls for confounding Reverse causation impossible Gold standard for assessing causality Randomized Control Trials Individuals free from exposure and disease Randomization Treatment Group Control Group Measure Outcome Measure Outcome

39. RCTs not always ethical or possible Fortunately nature has provided us with a natural randomized control trial ! Mendel’s law of independent assortment states that inheritance of a trait is independent (randomized) with respect to other traits Assessing the relationship between genotype, environmental risk factor and disease informs us on causality Therefore individuals are randomly assigned to three groups based on their genotype (AA, Aa, aa) independent of outcome Mendelian Randomization

40. If obesity causes CHD then the relationship between FTO and CHD should be estimated by the product of βFTO-Obesity and βObesity-CHD Mendelian Randomization Confounding Variables (smoking, diet etc.) Genetic Variant (FTO) βFTO-Obesity Modifiable Environmental Exposure Disease βObesity-CHD (Coronary Heart Disease) (Obesity)

41. If CHD causes obesity then βFTO-CHD should be zero. Mendelian Randomization Confounding Variables (smoking, diet etc.) Genetic Variant (FTO) βFTO-Obesity Modifiable Environmental Exposure Disease βObesity-CHD (Coronary Heart Disease) (Obesity)

42. If the relationship between Obesity and CHD is purely correlational (i.e. due to confounding) then βFTO-CHD should be 0 Mendelian Randomization Confounding Variables (smoking, diet etc.) Genetic Variant (FTO) βFTO-Obesity Modifiable Environmental Exposure Disease (Coronary Heart Disease) (Obesity)

43. Genotype is associated with the environmental exposure of interest Genotype is NOT associated with confounders Genotype is only related to its outcome via its association with the modifiable environmental exposure Mendelian Randomization Confounding Variables (smoking, diet etc.) Genetic Variant (FTO) Modifiable Environmental Exposure Disease (Coronary Heart Disease) (Obesity)

44. Mendelian Randomization is a way of using a genetic variant(s) to make causal inferences about (modifiable) environmental risk factors for disease and health related outcomes Environmental exposures (e.g. Obesity) can be modified ! Genetic factors cannot (at least for the moment…) Still a relatively new approach that has problems (i.e. finding genetic proxies for environmental exposures- multiple instruments?) Mendelian Randomization …but a LOT of scope for development…

45. Could SEM be used to enhance MR? Confounding Variables (smoking, diet etc.) Genetic Variant (FTO) Modifiable Environmental Exposure Disease (Coronary Heart Disease) (Obesity) Genetic Variant Genetic Variant (MC4R) (?)