What is a QTL? What are QTL?

Current methods for QTL  Single Marker Methods ( Student, 17?? )  t-tests  Interval Mapping Method (Lander and Botstein, Genetics 1989)  Mapping: estimate genetic maps  Locating QTL: based on genetic map  Composite Interval Mapping (Jansen 1993; Zeng 1993, 1994; Genetics)  Locating QTL: based on genetic map

 Understanding variability...  what causes it?  can we account for it?  Making predictions where there is uncertainty...  Drawing conclusions from incomplete information... Statistical Genetics/Genomics

microarray QTL protein microarray quantitative expression trait mapping (QTL) RW Doerge; Nature Reviews Genetics :43-52

Metabolism under phenotype Keurentjes, J.J.B. et al. Nat. Genet. 38, 842–849 (2006). | Article |Article

Clusters of metabolite level traits are controlled by pleiotropic QTL

…the big picture  There are “genes” or “regions’’ of DNA associated with traits, diseases, resistances of interest.  yield in corn  oil content in soybean  sugars of tomato  multiple sclerosis in human

Statistical Genetics aims to understand these genomic regions  Quantitative Trait Loci (QTL): genomic regions associated with a quantitative trait of interest.  Complex traits: are controlled by many QTL, often behaving differently in changing environments and under different conditions. …example

Major Quantitative Trait Locus Doebley & Stec (1991) Genetics

QTL mapping of many traits in the same RILs Annual Review of Plant Biology Vol. 55: (Volume publication date June 2004) (doi: /annurev.arplant ) First published online as a Review in Advance on December 12, 2003 NATURALLY OCCURRING GENETIC VARIATION IN ARABIDOPSIS THALIANA Maarten Koornneef,1 Carlos Alonso-Blanco,2 and Dick Vreugdenhil3

Cloned QTL in Arabidopsis LocusTraitMolecular function Functional polymorphism Functional alterationReferences CALInflorescence morphologyMADS TFSNPAltered protein(66) FRIFloweringUnknownINDELs (1-345 bp)Truncated protein (43, 58, 79) FLCFloweringMADS TFINDELs (1.2 to 4.2 kb) Expression level (43, 109) EDIFloweringCRY2 photoreceptorSNPAA substitution(37) PHYAHypocotyl lengthPHYA photoreceptorSNPAA substitution(99) PHYDFlowering/hypocotyl length PHYD photoreceptorINDEL (14 bp)Truncated protein (12) AOP2Glucosinolate biosynthesis 2-oxoglutarate- dioxygenase INDEL (5 bp)Expression level (71) AOP3Glucosinolate biosynthesis 2-oxoglutarate- dioxygenase UnknownExpression level (71) ESPGlucosinolate hydrolysisEpithiospecifier protein INDELs, SNPExpression level (87) MAM1Glucosinolate biosynthesis Methylthioalkylmalate synthase INDELs, (several kb) Deleted gene(80) MAM2Glucosinolates/insect resistance Methylthioalkylmalate synthase INDELs, (several kb) Deleted gene(80) RTM1Virus resistanceJacalin-like proteinSNPTruncated protein (30) RPS2Resistance to pseudomonas LRR proteinINDEL (10 bp), SNPTruncated protein (111) RPM1Resistance to pseudomonas LRR proteinINDEL (2.8 kb)Deleted gene(150)

Experimental Populations for QTL Study Backcross 1 Backcross 2 RW Doerge; Nature Reviews Genetics :43-52

Estimated Genetic Map (framework for QTL mapping) Chromosome 11 mouse RW Doerge; Nature Reviews Genetics :43-52; Butterfield et al., 1999; Journal of Immunology

Single Marker Analysis  Using a recombinant inbred (RI) or backcross population  there are two possible alleles at each marker  and, two genotypic classes per marker  RI: M1/M1 and M2/M2  Backcross: M1/M1 and M1/M2  Each individual: genotypic and phenotypic data  consider: marker M and trait Y  every marker has 2 states:  homozygous 1: M1/M1  heterozygous 2: M1/M2

 using a simple t-test  split individuals into marker classes  calculate  sample means and variances on Y  test for differences in means

…diamonds are the single markers RW Doerge. Nature Reviews Genetics :43-52

…take advantage of marker order Interval Mapping  Marker M: alleles M 1 and M 2  Marker N: alleles N 1 and N 2  Distance between M and R defined by recombination r  the values of r is estimated and known  M r N  Use the additional information from knowing ‘r’ to locate QTL Lander and Bostein Genetics

M N QQ r Locate QTL by stepping through the interval defined by M and N

With arrays markers are no longer limiting >10k SFPs

Composite Interval Mapping Multiple QTL Mapping (MQM) Y=x*b* + X B +E Y is a quantitative trait (gene expression) vector, i=1,…,n b* effects of the putative QTL being tested x* is an indicator variable specifying the probability of an individual being in the different genotypic classes for the supposed QTL, depending on the flanking markers which define the interval. B is the vector of effects of selected markers fitted in the model X is the design matrix for the selected markers E is the error vector cofactors Zeng 1993, 1994; Jansen 1993; Genetics interval mapping

QTL mapping methodology Churchill & Doerge 1994 interval mapping composite interval mapping permutation threshold single marker Fisher 1935; Thoday 1961;Lander and Botstein 1989;Zeng 1994;

Overall Summary  QTL methodology  Detect and locate QTL  Locating QTL depends on genetic map  Many statistical and genetic issues  Permutation thresholds are specific to experiment  eQTL utilize expression data as quantitative traits to map expression variation  molecular dissection of complex traits