1. QTL studies: past, present & (bright?) future

2. Overview A brief history of ‘genetic variation’ Summary of detected QTL –plants –livestock –humans Modelling distribution of QTL effects From QTL to causal mutations Three success stories

3. [Galton, 1889]

4. (early 1900s) Inheritance of quantitative traits Biometriciansvs.Mendelians (Pearson)(Bateson) The height vs. pea debate Do continuously varying traits have the same hereditary and evolutionary properties as discrete characters?

5. Yes! t [Fisher, Wright]

6. Multiple-factor hypothesis (Many) independently segregating loci –Continuous (Gaussian) distribution of genotypes Environmental variation –‘Regression towards mediocrity’ [Galton, 1889] trait in progeny is not the average of trait in parents R = h 2 S Linear models & multivariate normality –Livestock breeders [Henderson] –BLUP(A)

7. Three bi-allelic additive loci

9. © Jeremy Stockton

10. © Roslin Institute

11. Lynch & Walsh (1998) Summary of 52 experiments (222 traits), mostly from inbred founder lines – in 45% of traits a QTL explaining >20% of phenotypic variation – in 84% of traits all QTLs explained >20% of the phenotypic variation – in 33% of traits all QTLs explained >50% of the phenotypic variation

12. Reported QTL in pigs 15 experimental crosses –N from 200 to 1000 multiple QTL for growth, fatness, carcass traits and reproduction nearly all chromosomes covered QTL explain 3 to 20% of F 2 variance [Bidanel & Rothschild 2002]

13. Xyz : X = A (average), L (lumbar), R (last rib), T (tenth-rib), S (shoulder), M (mid-back), F (first-rib) backfat thickness at xx kg (k) or xx weeks (w) of age; Locus names (in bold characters) : MC4R = melanocortin-4 receptor locus; IGF2 = insulin growth factor 2; RYR1 = ryanodine receptor locus ; HFAB = heart fatty acid binding protein locus; PIT1 = regulatory factor locus; RN = “acid meat” locus. Backfat thickness [Bidanel & Rothschild 2002]

14. How many QTLs are there and how many can we detect? Theory –Distribution of effects & experimental sample size (Otto & Jones, 2000) Data –Model reported QTL effects from experiments (Hayes & Goddard 2001)

15. Potential distributions of allelic effects. Each curve describes a gamma distribution with mean µ = 1 but with different coefficients of variation (C). The QTL underlying a particular phenotypic difference represent draws from the appropriate distribution, as illustrated by the circles under the x-axis. Only those QTL above the threshold of detection (  = 0.8, thin vertical line) are likely to be detected (solid circles). Those below the threshold are likely to remain undetected (open circles). [Otto & Jones 2000]

16. The expected number of detected loci as a function of the number of underlying loci. The expected number of detected loci is equal to n times the fraction of the probability density function, g[x, µ, C] given by (13), that lies above. It is plotted as a function of the number of underlying loci for a bell-shaped distribution (C = 0.5; dot-dashed curve), an exponential distribution (C = 1; solid curve), and an L-shaped distribution (C = 2; dashed curve). (A)  = 10% of D, as was typical in our studies with a large number of QTL and 200 F 2 's. (B)  = 5% of D, as was typical in our studies with a large number of QTL and 500 F 2 's. [Otto & Jones 2000]

17. Distribution of QTL effects in livestock [Hayes and Goddard, 2001]

18. Proportion of genetic variance explained by QTLs [Hayes and Goddard, 2001]

19. From QTL to gene Paradigm –Linkage –Fine-mapping (IBD/LD) –Association –Function

20. Positional Cloning of Complex Traits LOD Sib pairs Chromosome Region Association Study Genetics Genomics Physical Mapping/ Sequencing Candidate Gene Selection/ Polymorphism Detection Mutation Characterization/ Functional Annotation

21. Identified causal polymorphisms 41 (< March 2004) –31 in mammals 17 outbred populations –14 in humans –2 in pigs (RN, IGF2) –1 in dairy cattle (DGAT1) Few ‘proven’ with functional assays or through transgenics [Korstanje & Piagen 2001; Glazier et al. 2002]

22. [Korstanje & Piagen 2002] Identified QTLs in mammals

23. [Korstanje & Piagen 2002]

24. [Glazier et al. 2002]

25. Botstein & Risch (2003), Nature Genetics Is the nature of genetic variation for quantitative traits different???

26. Three success stories of QTL identification in farm animals IGF2 in pigs DGAT in dairy cows Callipyge in sheep

27. Van Laere et al. (2003). Nature 425:832-836 QTL Linkage peak on chr. 2p for muscle mass –Wild Boar x Large White cross –Pietran x Large White cross IGF2 = candidate –IGF2 is paternally imprinted in mice and man QTL = paternally imprinted –Sire’s allele expressed [Nezer et al. 1999; Jeon et al. 1999]

28. Effects etc. Wild boar cross –20-30 % of variance explained –~3% difference in Lean Meat % Pietran cross –~2% difference in % Lean Cuts –~5 mm difference in backfat Confidence interval ~4 cM (= small!!!) No sequence variants in coding parts of IGF2 could explain the observed effects

29. [Nezer et al. 1999]

30. Fine-mapping using haplotype sharing (Nezer et al. 2003) Marker-assisted segregation analysis –Assume bi-allelic QTL –Assume that ‘favourable’ allele Q appeared by mutation or migration ~50-100 years ago –Assume known effect (2% of ‘lean cuts’) –Determine QTL genotype status of 20 boars –Look for shared haplotype on Q chromosomes Identified shared haplotype of ~250 kb –Contained 2 paternally imprinted genes (INS and IGF2)

31. Qq boars Q q QQ or qq boars Genotype deduced From Qq haplotypes

32. All Q chromosome share a 90 kb common haplotype not present on q chromosomes [Nezer et al. 2003]

33. Resequencing 3 Q and 8 q chromosomes for 28.5 Kb spanning INS-IGF2 identifies 33 putative QTN [M. Georges]

34. Resequencing a heterozygous, non-segregating Hampshire sire identifies a recombination excluding TH-IGF2(I1) (- 9 candidate QTN) [M. Georges]

35. Resequencing a heterozygous, non-segregating Large White x Meishan sire identifies the QTN [M. Georges]

36. Pig-q AGCCAGGGACGAGCCTGCCCGCGGCGGCAGCCGGGCCGCGGCTTCGCCTAGGCTCGCAGCGCGGGAGCGCGTGGGGCGCGGCGGCGGCGGGGAG Pig-Q.......................................................A...................................... Human....G.....T.......T.C...T...G..TC...............................AG...A.........A.T....AG...... Mouse...T.........T......C.......T...T....C..A................G...TCT...............A.G............ INSIGF2TH 1231234a54b6789 CpG island DMR1 q Q P208 (ref.) LW3 LRJ H205 H254 M220 LW1224 LW1461 LW209 LW419 LW197 EWB LW33361 LW463 JWB %(G+C) Genes Van Laere, Fig. 1A SWC9 14 QTN is guanine to adenine substitution in IGF2-intron3 nucleotide 3072

37. DGAT in dairy cows Genome scan suggested QTL for fat% in milk on chromosome 14 IBD fine-mapping reduced region to 3 cM Association / linkage disequilibrium identifies causative mutation Mutation is an amino acid changing SNP in the DGAT1 gene

38. There are large QTL out there! QTL explains > 50% (!) of genetic variance in fat% QTL allele is common QTL acts additively

39. Callipyge mutation in sheep (major gene, not QTL)

40. Gene action: “Polar overdominance” [Freking et al. 1998] [1 st allele from dad 2 nd from mum]

41. Callipyge summary Gene action impossible to work out without genetic markers Causal mutation is non-coding How common is imprinting for QTL?

42. [Glazier et al. 2002]