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QTL studies: past, present & (bright?) future . 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. [Galton, 1889].

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overview
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
early 1900s inheritance of quantitative traits
(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?

slide5
Yes!

t

[Fisher, Wright]

multiple factor hypothesis
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)
lynch walsh 1998
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
reported qtl in pigs
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]

slide13

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]

how many qtls are there and how many can we detect
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)
slide15

[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).

slide16

[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.

from qtl to gene
From QTL to gene
  • Paradigm
    • Linkage
    • Fine-mapping (IBD/LD)
    • Association
    • Function
slide20

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

identified causal polymorphisms
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]

slide22

Identified QTLs in mammals

[Korstanje & Piagen 2002]

slide25

Botstein & Risch (2003), Nature Genetics

Is the nature of genetic variation for quantitative traits

different???

three success stories of qtl identification in farm animals
Three success stories of QTL identification in farm animals
  • IGF2 in pigs
  • DGAT in dairy cows
  • Callipyge in sheep
van laere et al 2003 nature 425 832 836
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]

effects etc
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
fine mapping using haplotype sharing nezer et al 2003
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)
slide31

Qq boars

Q

q

QQ or qq boars

Genotype deduced

From Qq haplotypes

slide32

All Q chromosome share a 90 kb common haplotype not

present on q chromosomes

[Nezer et al. 2003]

resequencing 3 q and 8 q chromosomes for 28 5 kb spanning ins igf2 identifies 33 putative qtn
Resequencing 3 Q and 8 q chromosomes for 28.5 Kb spanning INS-IGF2 identifies 33 putative QTN

[M. Georges]

slide34
Resequencing a heterozygous, non-segregating Hampshire sire identifies a recombination excluding TH-IGF2(I1) (- 9 candidate QTN)

[M. Georges]

resequencing a heterozygous non segregating large white x meishan sire identifies the qtn
Resequencing a heterozygous, non-segregating Large White x Meishan sire identifies the QTN

[M. Georges]

slide36

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

dgat in dairy cows
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
slide38

There are large QTL out there!

QTL explains > 50% (!) of genetic variance in fat%

QTL allele is common

QTL acts additively

slide39

Callipyge mutation in sheep

(major gene, not QTL)

gene action polar overdominance
Gene action: “Polar overdominance”

[1st allele from dad 2nd from mum]

[Freking et al. 1998]

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