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QTL mapping

QTL mapping. Simple Mendelian traits are caused by a single locus, and come in the ‘ all-or-none ’ flavor. A Quantitative Trait is one in which many loci contribute. The phenotype can therefore vary in a ‘ quantitative ’ manner. Ades 2008, NHGRI. Modified from Mike White slides, 2010.

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QTL mapping

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  1. QTL mapping Simple Mendelian traits are caused by a single locus, and come in the ‘all-or-none’ flavor. A Quantitative Trait is one in which many loci contribute. The phenotype can therefore vary in a ‘quantitative’ manner. Ades 2008, NHGRI Modified from Mike White slides, 2010

  2. Goals of QTL mapping • To identify the loci that contribute to phenotypic variation • Cross two parents with extreme phenotypes • Score the progeny for the phenotype • Genotype the progeny at markers across the genome • Associate the observed phenotypic variation with the underlying genetic variation • Ultimate goal: identify causal polymorphisms that explain the phenotypic variation Ades 2008, NHGRI Modified from Mike White slides, 2010

  3. Backcross Phenotype: Drug tolerance 80% 20% viability Usually have at least 100 individuals Broman and Sen 2009

  4. Intercross Phenotype: Drug tolerance 80% 20% viability Broman and Sen 2009

  5. Backcross vs. Intercross • An intercross recovers all three possible genotypes (AA, BB, AB). This allows detection of dominance with both alleles and provides estimates of the degree of dominance. • A backcross has more power to detect QTL with fewer individuals. • A backcross may be the only possible scheme when crossing two different species.

  6. Genetic map: specific markersspaced across the genome • Markers can be: • SNPs at particular loci • Variable-length repeats • e.g. ALU repeats • ALL polymorphisms • (if have whole genomes) Ideally, markers should be spaced every 10-20 cM and span the whole genome

  7. Genotype data: Determine allele at all markers in each F2

  8. Phenotype data

  9. Statistical framework • Missing Data Problem • Use marker data to infer intervening genotypes • 2. Model Selection Problem • How do the QTL across the genome combine with the covariates to generate the phenotype? Broman and Sen 2009

  10. Marker regression: simple T-test (or ANOVA) at each marker Marker 1: no QTL Marker 2: significant QTL (population means are different)

  11. Marker regression Advantages: • Simple test – standard T-test/ANOVA • Covariates (e.g. Gender, Environment) are to incorporate • No genetic map necessary, since test is done separately on each marker Disadvantages: • Any individuals with missing marker data must be omitted from analysis • Does not effectively consider positions between markers • Does not test for genetic interactions (e.g. epistasis) • The effect size of the QTL (i.e. power to detect QTL) is reduced by incomplete • linkage to the marker • Difficult to pinpoint QTL position, since only the marker positions are considered

  12. Interval mapping • Lander and Botstein 1989 • In addition to examining phenotype-genotype associations at markers, look for associations between makers by inferring the genotype A A A A Q • The methods for calculating genotype probabilities between markers typically use hidden Markov models to account for additional factors, such as genotyping errors

  13. Interval mapping Broman and Sen 2009

  14. Interval mapping – maximum likelihood 1. Calculate genotype probabilities at intervening locations for every individual A A A A • At a marker, calculate the conditional probability that an individual is in one of the two QTL genotype groups (AA or AB) given their phenotype and the current estimates of µAA(s-1)and µAB(s-1)(Expectation Step) • Calculate new estimates of µAA(s)and µAB(s),by combining the genotype probabilities of each individual with their phenotypic values (Maximization Step) • Repeat until the estimates of µAA(s-1),µAA(s)and µAB(s-1),µAB(s) converge.

  15. Interval mapping Advantages: • Takes account of missing genotype information – all individuals are included • Can scan for QTL at locations in between markers • QTL effects are better estimated Disadvantages: • More computation time required • Still only a single-QTL model – cannot separate linked QTL or examine for interactions among QTL

  16. LOD scores • Measure of the strength of evidence for the presence of a QTL • at each marker location LOD(λ) = log10 likelihood ratio comparing the hypothesis of a QTL at position λ versus that of no QTL } { Pr(y|QTL at λ, µAAλ,µABλ,σλ) log10 Pr(y|no QTL, µ,σ) LOD 3 means that the TOP model is 103 times more likely than the BOTTOM model Phenotype

  17. LOD curves How do you know which peaks are really significant?

  18. LOD threshold • Consider the null hypothesis that there are no QTLs genome-wide one location genome-wide Randomize the phenotype labels on the relative to the genotypes Conduct interval mapping and determine what the maximum LOD score is genome-wide Repeat a large number of times (1000-10,000) to generate a null distribution of maximum LOD scores Broman and Sen 2009

  19. Leoine Moyle, Indiana University  “Dissecting Speciation via the Genetics of Isolation and Adaptation” Genetics Colloquium Wednesday, March 14 3:30 pm Biotech Center Auditorium Room 1111

  20. LOD threshold • 1000 permutations • 10% False Discovery Rate = LOD 3.19 • (means that at this LOD cutoff 10% of peaks could be random chance) • 5% FDR = LOD 3.52 • Boundary of the peak is often taken as points that cross (Max LOD – 1.5) (or - 1.8 for an intercross)

  21. LOD curves – Marker regression vs. interval mapping IM MR • With complete marker genotype information, marker regression would give the same results as interval mapping

  22. Other mapping methods • Methods discussed assume single QTL models • Multiple QTLs on a chromosome are not estimated correctly • Cannot detect a QTL whose effect is dependent on the genotype at a second QTL (epistasis) Can also apply other Models • Two-dimensional two-QTL scans • Consider all pairs of markers across the genome • Multiple QTL Models • Jointly estimate all sets of QTL, interactions, and covariates in a single, coherent model • Focuses on the model selection problem of QTL mapping

  23. From QTL to candidate genes • F2 mapping results in large loci associated with the phenotype • Mapping a QTL that explains 5% of the phenotypic variance in 300 F2 animals will yield a region approximately 40 cM in size (800 genes in mice!) • 2050 mouse and 700 rat QTL have been mapped (reviewed in Flint et al. 2005) • ~20 underlying genes have been identified • Strategies for getting to causal loci: • Generate additional recombinants to fine map QTL • Effect sizes of QTL can be overestimated • Often one large QTL is composed of manly tightly linked QTL of small effect • Identify candidate genes from known mutants, tissue-specific expression, etc. • Identify candidate genes through comparison to association mapping studies or population genomics studies • Are the results repeatable across environments? • Association mapping and population genomics approaches only identify alleles with large effect sizes

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