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

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 Biometricians vs. 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 = h2 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 F2 variance [Bidanel & Rothschild 2002]

13. Backfat thickness 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. [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. [Otto & Jones 2000] 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 (q = 0.8, thin vertical line) are likely to be detected (solid circles). Those below the threshold are likely to remain undetected (open circles).

16. [Otto & Jones 2000] 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) q = 10% of D, as was typical in our studies with a large number of QTL and 200 F2's. (B) q = 5% of D, as was typical in our studies with a large number of QTL and 500 F2's.

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

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. Identified QTLs in mammals [Korstanje & Piagen 2002]

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. SWC9 TH INS IGF2 Genes 1 2 3 1 2 3 4a 4b 5 6 7 8 9 14 %(G+C) CpG island P208 (ref.) LW3 LRJ H205 H254 DMR1 Q M220 LW1224 LW1461 LW209 LW419 LW197 EWB LW33361 LW463 JWB q 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............ QTN is guanine to adenine substitution in IGF2-intron3 nucleotide 3072 Van Laere, Fig. 1A

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” [1st allele from dad 2nd from mum] [Freking et al. 1998]

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]