Genetic Traits Quantitative (height, weight) Dichotomous (affected/unaffected) Factorial (blood group) Mendelian - controlled by single gene (cystic fibrosis) Complex – controlled by multiple genes*environment (diabetes, asthma)

Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus chromosome genes

Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus QTG: Quantitative Trait Gene chromosome

Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus QTG: Quantitative Trait Gene QTN: Quantitative Trait Nucleotide chromosome SNP: Single Nucleotide Polymorphism

Association Studies Compare unrelated individuals from a population Phenotypes: –Cases vs Controls –Quantitative measure Genotypes: state of genome at multiple variable locations (Single Nucleotide Polymorphism = SNP) in each individual Seek correlation between genotype and phenotype

Problems with Association Studies Population stratification Linkage Disequilibrium Allele Frequencies Multiple loci Small Effect Sizes Very few Successes

Population Stratification If the sampling population comprises genetically distinct sub-populations with different disease prevalences Then - Any variant that distinguishes the sub- populations is likely to show disease association

Admixture Mapping Population is homogeneous but each individual’s genome is a mosaic of segments from different populations May be used to map disease loci –multiple sclerosis susceptibility –Reich et al 2005, Nature Genetics

Linkage Disequilibrium Mouse

Effects of Linkage Disequilibrium Correlation between nearby SNPs SNPs near to QTN will show association –Risk of false positive interpretation –But need only genotype “tagging” SNPs –~ 1 million tagging SNPs will be in LD with ~50% of common variants in the human genome

The Common-Disease Common- Variant Hypothesis Says –disease-predisposing variants will exist at relatively high frequency (i.e. >1%) in the population. –are ancient alleles occurring on specific haplotypes. –detectable in an case-control study using tagging SNPs. Alternative hypothesis says –disease-predisposing alleles are sporadic new mutations, perhaps around the same genes, on different haplotypes. –families with history of the same disease owe their condition to different mutations events. –Theoretically detectable with family-based strategies which do not assume a common origin for the disease alleles, but are harder to detect with case-control studies (Pritchard, 2001).

Power Depends on Disease-predisposing allele’s –Effect Size (Odds Ratio) –Allele frequency Sample Size: #cases, #controls Number of tagging SNPs To detect an allele with odds ratio of 1.25 and with allele frequency > 1%, at 5% Bonferroni genome-wide significance and 80% power, we require –~ 6000 cases, 6000 controls –~ 0.5 million tagging SNPs, one of which must be in perfect LD with the causative variant –[Hirschorn and Daly 2005]

WTCCC Wellcome Trust Case-Control Consortium 2000 cases from each of –Type I Diabetes –Type II Diabetes –rheumatoid arthritis, –susceptibility to TB –bipolar depression –…. and others … 3000 common controls million SNPs ~10 billion genotypes Data expected mid 2006

Mouse Models

Map in Human or Animal Models ? Disease studied directly Population and environment stratification Very many SNPs (1,000,000?) required Hard to detect trait loci – very large sample sizes required to detect loci of small effect (5,000-10,000) Potentially very high mapping resolution – single gene Very Expensive Animal Model required Population and environment controlled Fewer SNPs required (~ ,000) Easy to detect QTL with ~500 animals Poorer mapping resolution – 1Mb (10 genes) Relatively inexpensive

QTL Mapping in Mice using Inbred Line Crosses Genetically Homozygous – genome is fixed, breed true. Standard Inbred Strains available Haplotype diversity is controlled far more than in human association studies QTL detection is very easy QTL fine mapping is hard

Sizes of Mapped Behavioural QTL in rodents (% of total phenotypic variance)

Physiological QTL

Effect sizes of cloned genes

QTL detection: F2 Intercross A B X

QTL mapping: F2 Intercross A B X X F1

QTL mapping: F2 Intercross A B X X F1F2

QTL mapping: F2 Intercross +1 F F1 QTL

QTL mapping: F2 Intercross +1 F F1

QTL mapping: F2 Intercross F Genotype a skeleton of markers across genome 20cM

QTL mapping: F2 Intercross F ABAAABBA ABBAABBA ABBABABA BABABAAA BBBBABAA BABABAAA

QTL mapping: F2 Intercross F ABAAABBA ABBAABBA ABBABABA BABABAAA BBBBABAA BABABAAA

Single Marker Association Test of association between genotype and trait at each marker position. ANOVA F2 crosses are –good for detecting QTL –bad for fine-mapping –typical mapping resolution 1/3 chromosome – cM

Increasing mapping resolution Increase number of recombinants: –more animals –more generations in cross

Heterogeneous Stocks cross 8 inbred strains for >10 generations

Heterogeneous Stocks cross 8 inbred strains for >10 generations

Heterogeneous Stocks cross 8 inbred strains for >10 generations 0.25 cM

Mosaic Crosses foundersG3GNGNF20 mixingchopping up inbreeding F2, diallele HS, AI, outbreds RI (RIHS, CC)

chromosome markers Want to predict ancestral strain from genotype We know the alleles in the founder strains Single marker association lacks power, can’t distinguish all strains Multipoint analysis – combine data from neighbouring markers alleles Analysis of mosaic crosses

chromosome markers alleles Analysis of mosaic crosses Hidden Markov model HAPPY Hidden states = ancestral strains Observed states = genotypes Unknown phase of genotypes - analyse both chromosomes simultaneously Output is probability that a locus is descended from a pair of strains Mott et al 2000 PNAS

Testing for a QTL p iL (s,t) = Prob( animal i is descended from strains s,t at locus L) p iL (s,t) calculated using –genotype data –founder strains’ alleles Phenotype is modelled y i =  s,t p iL (s,t)T(s,t) + Covariates i + e i Test for no QTL at locus L –H 0 : T(s,t) are all same –ANOVA –partial F test

Example: Open Field Avtivity Mouse Model for Anxiety

OFA Tracking

Talbot et al 1999, Mott et al 2000 multipoint singlepoint significance threshold

Relation Between Marker and Genetic Effect No effect observable Observable effect QTL Marker 2 Marker 1

How Much Mapping Resolution do we need?

Mapping Resolution in Mouse QTL experiments F2 –~25-50 Mb [ genes] HS –1-5 Mb [10-50 genes] Need More Resolution

Other Outbred Populations Commercially available outbreds may contain more historical recombination Potentially finer mapping resolution How to exploit it ?

MF1 Outbred Mice MF1

Analysis of MF1

Single Marker Analysis

Unknown progenitors Sometime in the 1970’s…. LACA x CF MF1

MF1 resemble HS Sequencing revealed very few new variants in MF1 compared to HS strains Variants present in HS strains also present in MF1

MF1 as a mosaic of inbred strains

Mapping with 30 generation HS

Mapping with MF1 mice Yalcin et al 2004 Nature Genetics

Acknowledgements Jonathan Flint Binnaz Yalcin William Valdar Leah Solberg

Further Reading Mouse –Flint et al Nature Reviews Genetics 2005 Human –Hirschhorn and Daly, Nature Reviews Genetics 2005 –Zondervan and Cardon, Nature Reviews Genetics 2004