Zoology 2005 Part 2 Richard Mott Inbred

Zoology 2005 Part 2 Richard Mott

Inbred Mouse Strain Haplotype Structure • When the genomes of a pair of inbred strains are compared, – we find a mosaic of segments of identity and difference (Wade et al, Nature 2002). – A QTL segregating between the strains must lie in a region of sequence difference. • What happens when we compare more than two strains simultaneously?

No Simple Haplotype Block Mosaic Yalcin et al 2004 PNAS

…But a Tree Mosaic

In-silico Mapping • Simple idea– Collect phenotypes across a set of inbred strains – Genotype the strains (ONCE) – Look for phenotype-genotype correlation – Works well for simple Mendelian traits (eg coat colour) – Suggested as a panacea for QTL mapping

In-silico Mapping Problems • Less well-suited for complex traits • Number of strains required grows quickly with the complexity of the trait. Suggested at least 100 strains required, possibly more if epistasis is present • Require high-density genotype/sequence data to ensure identity-by-state = identity by-descent • May be very useful for the dissection of a QTL previously identified in a F 2 cross (look for patterns of sequence difference)

Recombinant Inbred Lines • Panels of inbred lines descended form pairs of inbred strains • Genomes are inbred mosaics of the founders • Lines only need be genotyped once • Similar to in-silico mapping except – identity-by-descent=identity-by-state – Coarser recombination structure – ? lower resolution mapping?

BXD chromosome 4

Testing if a variant is functional without genotyping it (Yalcin et al, Genetics 2005) • Requirements: – A Heterogeneous Stock, genotyped at a skeleton of markers – The genome sequences of the progenitor strains – A statistical test

Merge Analysis • Each polymorphism groups together the founders according to their alleles • If the polymorphism is functional, then a model in which the phenotypic strain effects are estimated after merging the strains together should be as good as a model where each strain can have an independent effect. • Compare the fit of “merged” and “unmerged” genetic models to test if the variant is functional. • If the fit of the merged model is poor then that variant can be eliminated.

Merge Analysis

How can we show a gene under a QTL peak affects the trait? • Genetic Mapping identifies Functional Variants, not Genes • Could be a control element affecting some other gene

Quantitative Complementation KO 0

Quantitative Complementation KO 0 Low 50 High 100 wt 30

Quantitative Complementation KO 0 Low 50 High 100 wt 30 d

Quantitative Complementation KO 0 Low 50 High 100 wt 30 d d D=d-d

Using Functional Information to Confirm Genes • Further experiments – further bioinformatics, eg networks, functional annotation (GO, KEGG) – candidate gene sequencing – gene expression analyses (e. QTL) of • founder strains • HS

Mouse/human sequence comparison

Enhancer reporter assays enhancer promoter luciferase reporter

Enhancer elements affect promoter expression

Large-Scale Genetic Mapping • Using a Heterogeneous Stock • Multiple Phenotypes collected in parallel

Predictions (from simulation of an HS population) • In a population of 1, 000 HS animals: – Genome-wide power to detect 5% QTL ~ 0. 92 – Resolution < 2 Mb

Study design • 2, 000 mice • 15, 000 diallelic markers • More than 100 phenotypes – each mouse subject to a battery of tests spread over weeks 5 -9 of the animal’s life – more (post-mortem) phenotypes being added

Phenotypes

Covariates • For each phenotype, we recorded covariates, eg, – experimenter – time of day – apparatus (eg, Shock Chamber 3)

Data collection • All animals microchipped • Automated data checking, processing and uploading • All data uploaded into the Integrated Genotyping System (IGS) database

Genotypes from Illumina • Genotyped and phenotyped 2, 000 offspring • Genotyped 300 parents • Pedigree analysis shows genotyping was 99. 99% accurate • 11, 558 markers polymorphic in HS

QTL mapping • Models – HAPPY and single marker association • Fitting framework – Linear regression of (transformed) phenotypes – Survival analysis for latency data – Logit-based models for categorical data • Significant covariates incorporated into the null model, eg Null = Startle ~ Test. Chamber + Body. Weight + Year + Age + Hour + Gender Additive Null + additive genetic info for locus Full Null + full genetic info for locus

QTL mapping • Significance tests – partial F-test (linear models), Chi-square / LRT (others) • Significance thresholds – different for each phenotype – have to take into account LD • fit distribution to scores of permuted data

E-values • We set score thresholds using ideas from sequence databank search programs such as BLAST

E-values • We set score thresholds using ideas from sequence databank search programs such as BLAST • The E-value of a threshold is the number of times you would expect to see a false positive exceed the threshold in a genome scan

E-values • We set score thresholds using ideas from sequence databank search programs such as BLAST • The E-value of a threshold is the number of times you would expect to see a false positive exceed the threshold in a genome scan • Applying the Bonferroni correction to the number of marker intervals is too severe because LD makes neighbouring scores correlated.

E-values • We set score thresholds using ideas from sequence databank search programs such as BLAST • The E-value of a threshold is the number of times you would expect to see a false positive exceed the threshold in a genome scan • Applying the Bonferroni correction to the number of marker intervals is too severe because LD makes neighbouring scores correlated. • Permutation analyses indicate the score of the most significant expected random score amongst all ~12000 marker intervals behaves as if it was drawn from M~4000 independent tests.

E-values • We set score thresholds using ideas from sequence databank search programs such as BLAST • The E-value of a threshold is the number of times you would expect to see a false positive exceed the threshold in a genome scan • Applying the Bonferroni correction to the number of marker intervals is too severe because LD makes neighbouring scores correlated. • Permutation analyses indicate the score of the most significant expected random score amongst all ~12000 marker intervals behaves as if it was drawn from M~4000 independent tests. • Hence a nominal P-value of p corresponds to an E-value of p. M

Problems Our population includes both siblings and unrelateds • We have ignored this distinction And therefore: 1. 2. Confounding environmental family effects with genetic family effects Allowing ghost peaks due to linkage disequilibrium between markers within a sibship Our solution so far: (1) Investigating the effect of environmental factors and building covariates into the model (2) Identify peaks by a multiple conditional fit

Multiple Peak Fitting Forward Selection • For each phenotype’s genome scan: – Make list of all peaks > genome-wide threshold T – Fit most significant peak, P 1 – Go through list of peaks, refitting each on conditional upon the most significant peak. – Add the most significant remaining peak, P 2 – Continue refitting remaining peaks P 3 , P 4 … and adding them into model until the most significant remaining peak < T

Peaks found by multiple conditional fit Multiple conditional fit (using additive model only) number of phenotypes

Database for scans

Database for scans Additive model Full model E-value thresholds • additive only • E<0. 01 is about the same as genome-wide corrected p<0. 01.

Database for scans zoom in

Covariates

QTL Mapping: Validation • Coat colour • Detection of known QTLs

Coat colour genes

A known QTL: HDL Wang et al, 2003 HS mapping

High Resolution QTLs Phenotype Chrom Mb Method Ref HS position Cue freezing 3 70 -83 Genome tagged mice Liu 2003 71 -73 Obesity 2 142 -168 Congenic Demant 2004 150 -153 10 week body mass 1 156 -160 Progeny testing Christians 2004 154. 5 -156 Emotionality 1 143 -148 HS Mott 2000 143 -144. 5 Emotionality 10 123 -127 HS Mott 2000 121. 5 -122. 7 Emotionality 12 54 -57 HS Mott 2000 55. 5 -56. 5 Emotionality 15 64 -77 HS Turri 1999 63. 5 -66

New QTLs: two examples • Freeze. During. Tone (from Cue Conditioning behavioural experiment) ………… 1 peak • % of CD 4 in CD 3 cells (immunology assay) ………… 10 peaks

Cue Conditioning Freezing • Freezing in response to a conditioned stimulus TONE

Cue Conditioning • Freeze. During. Tone: huge effect, small chr 15 number of genes cntn 1: Contactin precursor (Neural cell surface protein)

% CD 4 cells in CD 3 cells • huge effect but lots of genes

% CD 4 in CD 3 (under peak)

All QTLs • 608 peaks • Median interval is 938, 936 bp … • … or about 9 genes per peak

Summary • The HS project so far has – phenotyped 2, 500 HS mice – genotyped 2, 300 mice – mapped over 140 phenotypes – identified more than 600 potential QTLs

Confirming gene candidates • Increased mapping resolution through – – – include epistasis multivariate Gx. E pleiotropy sex effects • Further experiments – further bioinformatics, eg networks, functional annotation (GO, KEGG) – candidate gene sequencing – gene expression analyses (e. QTL) of • founder strains • HS

Confirming gene candidates: epistasis Single marker association of pairwise epistasis

Work of many hands Carmen Arboleda-Hitas Amarjit Bhomra Peter Burns Richard Copley Stuart Davidson Simon Fiddy Jonathan Flint Polinka Hernandez Sue Miller Richard Mott Chela Nunez Gemma Peachey Sagiv Shifman Leah Solberg Amy Taylor Martin Taylor Jordana Tzenova-Bell William Valdar Binnaz Yalcin Dave Bannerman Shoumo Bhattacharya Bill Cookson Rob Deacon Dominique Gauguier Doug Higgs Tertius Hough Paul Klenerman Nick Rawlins Project funded by The Wellcome Trust, UK