GWAS for complex traits: where is the hidden heritability? Andrea Vilarrubí Porta

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

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

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?

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

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

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

▪ 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

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 )

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

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

▪ 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

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

✓ 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

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

Thank you for your attention!!