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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