gwas for complex traits: where is the hidden...
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GWAS for complex traits: where is the hidden heritability? Andrea Vilarrubí Porta
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Contents1. Introduction
1.1 Genetic determination of a phenotype
1.2 Heritability: What is the missing heritability?
2. GWAS: Genome wide association studies
2.1 GWAS era
2.2 GWAS studies
2.2 GWAS Limitations: How to narrow the gap?
3. Concept with potential of narrowing the gap
3.1 Omnigenic model for complex disease
4. Conclusions
5. References
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GENOTYPE
ENVIROMENT
PHENOTYPE
Genetic determination of a phenotype
How genetic variation contributes to phenotypic variation? Monogenic/Mendelian traits
Polygenic/complex traits
Gene Gene 1 Gene 2 Gene 3
Mutation Genetic variation
Abnormal protein Abnormal protein network
Inheritance pattern Inheritance pattern
Mendelian; Dominant Recessive; X-linked
Non-mendelian; Complex; Susceptibility
Phenotypic expression and family
risk Phenotypic expression and family risk
100% Mendelian genetics
Variable genetic risk, associated with environmental factors
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TRAIT HERITABILITY
Height 0.86
Blood pressure 0.8
Body mass index 0.6
Type I diabetesType II diabetes
0.880.65
Hare Lip 0.76
Depression 0.45
Schizophrenia 0.81
Heritability = Genetic variance/ Phenotypic variance
REF: Sadee, W., Hartmann, K., Seweryn, M., Pietrzak, M., Handelman, S. K., & Rempala, G. A. (2014). Missing heritabilityof common diseases and treatments outside the protein-coding exome. Human Genetics, 133(10), 1199–215Marouli, E. et al. (2017) Rare and low-frequency coding variants alter human adult height. Nature 2017 542:186-190
Missing/Hidden Heritability
▪ Genomics of complex diseases: unresolved
▪ Genetic factors identified only explain a small portionof heritability estimation
▪ Heritability
H2
h2 Additive effect of individualalleles
Epistasis + epigenetics
Heritability: What is the missing heritability?
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Only 20% of estimated heritability explained by the combination of all significant SNPs
SNPs with small individual effects/ low frequent hidden in GWAS
Heritability: What is the missing heritability?
REF: Marouli, E. et al. (2017) Rare and low-frequency coding variants alter humanadult height. Nature 2017 542:186-190
Height: the best-fitline estimates that3.8% of SNPs havecausal effects
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GWAS: Genome wide association studies
Beginning of the GWAS era: 2007
▪ Based on the concept that genetic variationshows considerable linkage disequilibrium: Agiven SNP is strongly correlated with otherSNPs
▪ GWAS tests a single Tag SNP from regions ofLD to mark the zones in the genome showingdisease association
▪ Facilitated by the HapMap project (2002-2005)
REF: Manolio, T.A. (2017) In Retrospect: A decade of shared genomic associations. Nature 2017 546:360-361
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GWAS: studies
In a typical study 500- 1000K SNPs are tested 0,6 –1,2% of the SNPs already knownin the human genome (2015, 1000 Genome Project) SNP accepted =p-value ≤ 5.0 ×10-8 Problem?
REF: Gibson, G. (2010). Hints of hidden heritability in GWAS. Nature Genetics, 42(7), 558–60. GWAS catalog: 5267 SNP-trait associations
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▪ The most important loci in genome have small effect sizes and only explain a modest fraction ofthe predicted genetic variance: GAPMystery of the missing heritability.
▪ Common SNPs with sizes effects well below genome/wide statistical significance account formost of the hidden heritability of many traits
▪ Rare variants with larger effect sizes also contribute with major fitness consequences
▪ Complex traits are mainly driven by noncoding variants that presumably affect gene regulation.
GWAS Limitations: How to narrow the gap?
Paradigm: complex diseases are driven by an accumulation of weak effects on the key genes and regulatory pathways that drive disease risk
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GWAS Limitations: How to narrow the gap?
1. LIMITATIONS OF GWA (Rare variants)
2. ‘OUT OF SIGHT’ (Low penetrance)
3. GENOME ARCHITECTURE (Structural variation:
CNVs, rSNPs and srSNPs)
4. GENE NETWORKS (Epistasis)
5. HERITABILITY ESTIMATIONS ON DOUBT
(Epigenetics)
6. LOST ON DIAGNOSIS (Rare variants, common
disease: Different diseases )
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Property of Network: ‘‘Small world’’
▪ Core genes: small part of heritability
▪ Peripheral genes: main part of heritability
Concept with potential of narrowing the gap
Any gene that is expressed in a disease-relevant tissue is likely to be just a few steps from one or more coregenes. Consequently, any variant that affects expression of a ‘‘peripheral’’ gene is likely to have non-zeroeffects on regulation of the core genes and thereby incur a small effect on disease risk
OMNIGENIC MODEL
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Omnigenic model for complex disease
For any given complex disease phenotype:
▪ A limited number of genes have directeffects on disease risk
▪ By the small world property ofnetworks: most expressed genes areonly a few steps from the nearest coregene and thus may have non-zeroeffects on disease
▪ Most heritability comes from geneswith indirect effects
REF: Boyle, E.A., Li, Y.I. and Pritchard, J.K. (2017) An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell 2017
169:1177-1186
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▪ Diseases are generally associated with
dysfunction of specific tissues
▪ The overall effect size of any given SNP
would be a weighted average of its effects
in each cell type.
Omnigenic model for complex disease
REF: Boyle, E.A., Li, Y.I. and Pritchard, J.K. (2017) An Expanded View of Complex Traits: From Polygenic to Omnigenic.
Cell 2017 169:1177-1186
The quantitative effect of any given variant would then be an average of its effect size
in each cell type, weighted by cell type importance
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Omnigenic model for complex disease ▪ Any gene with regulatory variants in at least one tissue
that contributes to disease pathogenesis is likely to havenon trivial effects on risk for that disease
▪ The relative effect sizes are such that, since core genesare hugely out numbered by peripheral genes, a largefraction of the total genetic contribution to diseasecomes from peripheral genes that do not play direct rolesin disease.
▪ It remains to be determined whether the effects ofnetwork pleiotropy would be strong enough to drivesignificant signals in practice, especially if the core genesare far apart in the network
REF: Boyle, E.A., Li, Y.I. and Pritchard, J.K. (2017) An Expanded View of Complex Traits: From Polygenic to
Omnigenic. Cell 2017 169:1177-1186
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✓ Huge numbers of genes contribute to the heritability for complex diseases
✓ GWAS studies need to focus on the role of causative SNP, not only on marker SNP
✓ Cost of sequencing is steadily decreasing → Sequencing more individuals → more SNP dataof both common and rare SNPs
✓ Re-estimate heritability to contemplate the effects of environment, epigenetics, epistasis…
✓ Understanding the impact of very small effects in organismal systems: great need todevelop cell-based model systems that can recapitulate aspects of complex traits.
✓ Development of highly precise, high-throughput techniques for mapping networks indiverse cell types, especially at the protein level
✓ Very deep association data will be essential for developing personalized risk prediction:these data will be essential for modeling the flow of regulatory information through cellularnetworks
Conclusions
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Or Zuka, Eliana Hechtera, Shamil R. Sunyaeva and Eric S. Lander. 2012. The mystery of missing heritability:Genetic interactions create phantom heritability. PNAS 109:1193-1198
Wood, A et al. 2014. “Defining the role of common variation in the genomic and biological architecture of adulthuman height.” Nature Genetics. 46:1173-86. DOI:10.1038/ng.3097
Delude, C.M. (2015) Deep phenotyping: The details of disease. Nature 2015 527:S
Van der Klaauw, A.A. & Farooqi, I.S. (2015) The Hunger Genes: Pathways to Obesity. Cell 2015 161:119-132
Marouli, E. et al. (2017) Rare and low-frequency coding variants alter human adultheight. Nature 2017 542:186-190
Manolio, T.A. (2017) In Retrospect: A decade of shared genomic associations. Nature 2017 546:360-361
Boyle, E.A., Li, Y.I. and Pritchard, J.K. (2017) An Expanded View of Complex Traits: From Polygenic to Omnigenic.Cell 2017 169:1177-1186
References
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Thank you for your attention!!