Why this paper

• Causal genetic variants at loci contributing to complex phenotypes unknown

• Rat/mice model organisms in physiology and diseases

• Relevant to our work– Integration of GWAS of different traits– Interpretation of human GWAS

Advantages of genetic mapping using heterogeneous stocks

• Accuracy of QTL mapping to Mb resolution

• WGS imputation from progenitor genomes

• Haplotypes well defined– Single SNP vs haplotype (spatial) association

– Difficult in humans, large #of rare/unknown haplotypes

Design

Sequencing

AJ AKR Balb C3H C57 DBA IS RIII

HS

Random Breeding

HS Generation > 60

Reconstruction of rat genomes as mosaic of founder haplotypesbased on 265,551 SNPs (“sequence imputation”)

Genotypes

• 1,407 phenotyped NIH-HS animals• 198 parents (~14.2 litter size)• RATDIV genotyping array (13 inbred strains)

– 803,485 SNPs– 560,000 segregating in NIG-HS– 265,551 used for haplotype reconstruction

• Sequencing of founder samples– Number ?– 22x coverage

Phenotypes• 160 measurements

Sequencing

• 7.2M SNP• 633,000 indels• 44,000 structural variants

Sequencing• False Positives

• 2.7% SNP• 2.2% indels• 16.7% structural variants

• False Negatives• 17.2% SNPs• 41.4% indels• 65% structural variants

Nucleotide diversity in NIH-HS progenitors

• Similar diversity between strains

Nucleotide diversity in NIH-HS progenitors

• Similar diversity between strains• 29% SNP private to particular strain

– Unique haplotypes relatively common• Regions of low diversity are small (~400 kb)

Genotyping

QTL mapping

• Reconstruction of rat genomes as mosaics of founder haplotypes– R HAPPY

Svenson K L et al. Genetics 2012;190:437-447

QTL mapping

• Reconstruction of rat genomes as mosaics of founder haplotypes– R HAPPY. – Mixed Linear Model (EMMA, normal phenotypes)

– Resample model averaging (BAGPHENOTYPE,non-normal)• Non-parametric bootstrap aggregation (bagging)

Haplotype from strain s at locus l

random effectExpected number of haplotypes

Haplotype

Strain A B C------------------------------y1 = 2 0

0y2 = 0 2

0y3 = 0 1

1

QTL mapping

QTL results

• 355 QTLs for 122 phenotypes (avg. 2.9)

QTL results

QTL results

Haplotype (1)

Strain A B C------------------------------y1 = 2 0

0y2 = 0 2

0y3 = 0 1

1 Sequence variants

A BC

Strain CC CC TT------------------------------SDP 0 0

1

Merge analysesStrain distribution pattern (SDP)

ABC

ABC

= 0 0 1

= 1 0 0

Haplotype (1)

Strain A B C------------------------------y1 = 2 0

0y2 = 0 2

0y3 = 0 1

1

Sequence variants

Strain CC CC TT------------------------------y1 = 2 0

0y2 = 0 2

0y3 = 0 1

1Merge model (2)

Strain C T------------------------------y1 = 2 0y2 = 2 0y3 = 1 1

• (2) Sub model (1)• if QTL == single variant

• R2(2)~R2(1)• [logPmerge – logPhaplotype] > 0

Merge analyses

Merge analyses

• 343 QTLs– 131 (38%) at least 1 candidate variant

• Increased resolution– 90% of variants ruled out, d <0– Candidates in coding regions affecting protein

structure more likely to be causal – Eliminates candidate genes that are distant from

candidate variant

Merge analyses (examples)

• 3 QTL for patelet aggregation

Merge analyses (examples)

• Candidate variant in single gene

Merge analyses (examples)

• Candidate variant in coding region

Merge analysis

• Single variants rarely account for QTL effects– 212 (68%) QTL had no candidate variant

• Possible reasons– Causative variants missed in sequencing– QTL mapping biased towards QTL without

candidate variants – Merge underestimates statistical significance– Multiple causal variants

Merge analysis

– Causative variants missed in sequencing• Simulation of all possible SDPs for di-tri-allelic SNPs and

merge analysis• 168 (49%) would still have no causative variant

– Simulation different QTL architectures• Single variants• Multiple variants within gene, multiple variants linked

loci• Haplotype effects/ no individual variants

Merge analysis– Simulation of causal variants

Merge analysis

• Haplotype mapping overestimates QTL without causative variant (?)

• Merge analysis underestimates number of QTL without causative variant (?)– Multiple causative variants

Concordance between species• 38 measures common between NIG-HS and mice HS• Orthologous rarely contribute to the same

phenotype

Concordance between species• 38 measures common between NIG-HS and mice HS

• Orthologous rarely contribute to the same phenotype

• KEGG pathways for QTL associated genes in rat in mice only significantly enriched for “proportion of B cells”)

Discussion• Combining sequence with mapping data can identify candidate

loci• 50% of QTL can not be attributed to single causal variant

– Multiple causal variants, more complex models required– Rat QTL similar to Trans eQTL

• Not possible to accurately asses overlap between species– limited power of pathway analysis– limited power from comparing phenotypes (within species?)– Variants in orthologous genes rarely contribute to same phenotype