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Part 4
Examples from Genome-wide Study Analyses
(Weedon,
MN
et
al.
Nat
Genet.
2007)
Example Human Height: GWAS fail to describe the major variation
in phenotypes
Human height (80-90% heritable), but.. – >40 SNPs only
explain ~5% variation (Commentary: Brendan Maher, Nature, 2008) –
Top candidate HMGA2; 0.4 cm increased height pr. copy; ~0.3% of the
population’s variation
(Weedon, MN et al. Nat Genet. 2007)
Possible Explanations – Rare variants: common disease, common
variant hypothesis does not hold – Submicroscopic variation (>
0.1kb &
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Individuals were taken from the Nugenob Weight Loss Trial
‘Nutrient-Gene Interactions in Human Obesity’, coordinated by
Thorkild IA Sørensen, Institute of Preventive Medicine, Denmark
(Sørensen, TIA et al. PLoS Clin Trials 2006)
771 obese male and female unrelated obese European Caucasians
from 8 clinical centers in seven European countries
– Age 20-50 years – BMI > 29.5
Weight Loss Trial: Randomized trial of a hypo-energetic high-
versus low-fat diet – - 600 kcal/d – Fat energy 40-45% or 20-25%
– Same mean weight loss across diets (6.9 kg, 6.7 kg) – In total
648 completers (More dropouts on the high-fat diet)
Fat Oxidation Measurement: Test meal with energy content fixed
at 50% at the predicted basal metabolic rate. Indirect calorimetry
was used assess energy expenditure and substrate oxidation.
750 subjects were genotyped on Illumina HumanHap 300K SNP
arrays
There is Structure among Individuals in the Nugenob Cohort
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Weight Loss Phenotype does not co-localize with Ancestry
Fat Oxidation Phenotype does not co-localize with Ancestry
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The Regression Models in the GWAS are adjusted for various
variables
FatOxPP ~ gender + center + baseline FatOx + baseline FatOx2 +
SNPi
WL ~ gender + age + age2 + center + diet group + baseline weight
+ SNPi
WLDiet ~ gender + age + age2 + center + diet group + baseline
weight + SNPi + SNPi*diet group
Abbreviations and explanations: • FatOx; Fat oxidation capacity
• WL; Amount of kg’s lost in the 10 weak weight loss trial •
Center; Which European center the individual originates from •
Diet group; Either high fat or low fat diet
Manhattan Plot lacks Skyscrapers above the Bonferroni Skyline
Level
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Quantile-Quantile Plot shows that there is no major deviation
from random expectations
Enrichment of Effect Size, an increase of Individuals and/or
Integration reduce Chance Correlations
Challenge The human genome is so large that it is very likely to
incur a large amount of chance-correlation instead of causal SNPs
in the statistical analysis.
Solutions 1. Enrich the study cohort for the phenotype you are
looking for (Increase
the effect size of causal SNPs) 2. Increase number of genotyped
Individuals 3. Add independent phenotype-specific data (e.g. gene
expression data)
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Example on how Genetic Data may be integrated with GWAS Data
(Hansen,
Lage,
Pers
et
al,
SubmiIed)
Genes are scored based on the GWAS SNPs mapped to the Gene
(Ensembl
Genome
Browser,
www.ensembl.org)
SN
P
SN
P
SN
P
SN
P
SN
P
SN
P
Steps to score a gene 1) GWAS: SNPi -> P-valuei 2) Score
genex with the lowest p-value of all SNPs mapped to genex 3)
Correct genex score with the number of independent number of SNPs
mapped to genex
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Long Genes are more likely to harbour non-causal SNPs
correlating with the Phenotype by Chance
Gen
e Le
ngth
(bp
s)
# SN
Ps p
er g
ene
# SN
Ps p
er g
ene
Minimum SNP in Gene Minimum SNP in Gene Gene Length (bps)
Adjustment by independent number of SNPs per Gene corrects Gene
Length Bias
Gen
e Le
ngth
(bp
s)
# SN
Ps p
er g
ene
# SN
Ps p
er g
ene
Minimum SNP in Gene Minimum SNP in Gene Gene Length (bps)
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Permutation Analysis shows that Gene Length adjustment performs
better than a naïve Gene Scoring
The resulting Meta-rank prioritizes the Genome according to the
Phenotype
(Hansen,
Lage,
Pers
et
al,
SubmiIed)
