2. Genetic Traits Quantitative (height, weight) Dichotomous (affected/unaffected) Factorial (blood group) Mendelian - controlled by single gene (cystic fibrosis) Complex – controlled by multiple genes*environment (diabetes, asthma)

3. Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus chromosome genes

4. Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus QTG: Quantitative Trait Gene chromosome

5. Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus QTG: Quantitative Trait Gene QTN: Quantitative Trait Nucleotide chromosome SNP: Single Nucleotide Polymorphism

6. Association Studies Compare unrelated individuals from a population Phenotypes: –Cases vs Controls –Quantitative measure Genotypes: state of genome at multiple variable locations (Single Nucleotide Polymorphism = SNP) in each individual Seek correlation between genotype and phenotype

7. Problems with Association Studies Population stratification Linkage Disequilibrium Allele Frequencies Multiple loci Small Effect Sizes Very few Successes

8. Population Stratification If the sampling population comprises genetically distinct sub-populations with different disease prevalences Then - Any variant that distinguishes the sub- populations is likely to show disease association

9. Admixture Mapping Population is homogeneous but each individual’s genome is a mosaic of segments from different populations May be used to map disease loci –multiple sclerosis susceptibility –Reich et al 2005, Nature Genetics

10. Linkage Disequilibrium Mouse

11. Effects of Linkage Disequilibrium Correlation between nearby SNPs SNPs near to QTN will show association –Risk of false positive interpretation –But need only genotype “tagging” SNPs –~ 1 million tagging SNPs will be in LD with ~50% of common variants in the human genome

12. The Common-Disease Common- Variant Hypothesis Says –disease-predisposing variants will exist at relatively high frequency (i.e. >1%) in the population. –are ancient alleles occurring on specific haplotypes. –detectable in an case-control study using tagging SNPs. Alternative hypothesis says –disease-predisposing alleles are sporadic new mutations, perhaps around the same genes, on different haplotypes. –families with history of the same disease owe their condition to different mutations events. –Theoretically detectable with family-based strategies which do not assume a common origin for the disease alleles, but are harder to detect with case-control studies (Pritchard, 2001).

13. Power Depends on Disease-predisposing allele’s –Effect Size (Odds Ratio) –Allele frequency Sample Size: #cases, #controls Number of tagging SNPs To detect an allele with odds ratio of 1.25 and with allele frequency > 1%, at 5% Bonferroni genome-wide significance and 80% power, we require –~ 6000 cases, 6000 controls –~ 0.5 million tagging SNPs, one of which must be in perfect LD with the causative variant –[Hirschorn and Daly 2005]

14. WTCCC Wellcome Trust Case-Control Consortium 2000 cases from each of –Type I Diabetes –Type II Diabetes –rheumatoid arthritis, –susceptibility to TB –bipolar depression –…. and others … 3000 common controls 0.675 million SNPs ~10 billion genotypes Data expected mid 2006

15. Mouse Models

16. Map in Human or Animal Models ? Disease studied directly Population and environment stratification Very many SNPs (1,000,000?) required Hard to detect trait loci – very large sample sizes required to detect loci of small effect (5,000-10,000) Potentially very high mapping resolution – single gene Very Expensive Animal Model required Population and environment controlled Fewer SNPs required (~100- 10,000) Easy to detect QTL with ~500 animals Poorer mapping resolution – 1Mb (10 genes) Relatively inexpensive

17. QTL Mapping in Mice using Inbred Line Crosses Genetically Homozygous – genome is fixed, breed true. Standard Inbred Strains available Haplotype diversity is controlled far more than in human association studies QTL detection is very easy QTL fine mapping is hard

18. Sizes of Mapped Behavioural QTL in rodents (% of total phenotypic variance)

19. Physiological QTL

20. Effect sizes of cloned genes

21. QTL detection: F2 Intercross A B X

22. QTL mapping: F2 Intercross A B X X F1

23. QTL mapping: F2 Intercross A B X X F1F2

24. QTL mapping: F2 Intercross +1 F2 000+2-2 F1 QTL

25. QTL mapping: F2 Intercross +1 F2 000+2-2 F1

26. QTL mapping: F2 Intercross F2 00+2-2 Genotype a skeleton of markers across genome 20cM

27. QTL mapping: F2 Intercross F2 00+2-2 ABAAABBA ABBAABBA ABBABABA BABABAAA BBBBABAA BABABAAA

28. QTL mapping: F2 Intercross F2 00+2-2 ABAAABBA ABBAABBA ABBABABA BABABAAA BBBBABAA BABABAAA

29. Single Marker Association Test of association between genotype and trait at each marker position. ANOVA F2 crosses are –good for detecting QTL –bad for fine-mapping –typical mapping resolution 1/3 chromosome – 20-30 cM

30. Increasing mapping resolution Increase number of recombinants: –more animals –more generations in cross

31. Heterogeneous Stocks cross 8 inbred strains for >10 generations

32. Heterogeneous Stocks cross 8 inbred strains for >10 generations

33. Heterogeneous Stocks cross 8 inbred strains for >10 generations 0.25 cM

34. Mosaic Crosses foundersG3GNGNF20 mixingchopping up inbreeding F2, diallele HS, AI, outbreds RI (RIHS, CC)

35. chromosome markers Want to predict ancestral strain from genotype We know the alleles in the founder strains Single marker association lacks power, can’t distinguish all strains Multipoint analysis – combine data from neighbouring markers alleles 1121 1 2111221221111211211111212212 1 Analysis of mosaic crosses

36. chromosome markers alleles 1121 1 2111221221111211211111212212 1 Analysis of mosaic crosses Hidden Markov model HAPPY Hidden states = ancestral strains Observed states = genotypes Unknown phase of genotypes - analyse both chromosomes simultaneously Output is probability that a locus is descended from a pair of strains Mott et al 2000 PNAS

37. Testing for a QTL p iL (s,t) = Prob( animal i is descended from strains s,t at locus L) p iL (s,t) calculated using –genotype data –founder strains’ alleles Phenotype is modelled y i =  s,t p iL (s,t)T(s,t) + Covariates i + e i Test for no QTL at locus L –H 0 : T(s,t) are all same –ANOVA –partial F test

38. Example: Open Field Avtivity Mouse Model for Anxiety

40. OFA Tracking

42. Talbot et al 1999, Mott et al 2000 multipoint singlepoint significance threshold

44. Relation Between Marker and Genetic Effect No effect observable Observable effect QTL Marker 2 Marker 1

45. How Much Mapping Resolution do we need?

46. Mapping Resolution in Mouse QTL experiments F2 –~25-50 Mb [250-300 genes] HS –1-5 Mb [10-50 genes] Need More Resolution

47. Other Outbred Populations Commercially available outbreds may contain more historical recombination Potentially finer mapping resolution How to exploit it ?

48. MF1 Outbred Mice MF1

49. Analysis of MF1

50. Single Marker Analysis

51. Unknown progenitors Sometime in the 1970’s…. LACA x CF MF1

52. MF1 resemble HS Sequencing revealed very few new variants in MF1 compared to HS strains Variants present in HS strains also present in MF1

53. MF1 as a mosaic of inbred strains

54. Mapping with 30 generation HS

55. Mapping with MF1 mice Yalcin et al 2004 Nature Genetics

56. Acknowledgements Jonathan Flint Binnaz Yalcin William Valdar Leah Solberg

57. Further Reading Mouse –Flint et al Nature Reviews Genetics 2005 Human –Hirschhorn and Daly, Nature Reviews Genetics 2005 –Zondervan and Cardon, Nature Reviews Genetics 2004