SNPs and complex traits: where is the hidden heritability? Jorge de los Santos Castela MSc in Advanced Genetics, Universitat Autònoma de Barcelona. Dec 22, 2016.

Outline 1. Introduction. SNPs 2. GWAS 3. GWAS and hidden heritability 4. Hidden heritability examples

Where’s heritability hiding? ‘Heritability is a concept that summarizes how much of the variation in a trait is due to variation in genetic factors.’ Wray, N. & Visscher, P. (2008) Estimating trait heritability. Nature Education 1(1):29. Even though we know ‘a lot’ of genetic variants for most common traits or genetic disorders (from height to Crohn’s disease) they explain a very small percentage of the contribution to the variance. Where’s heritability hiding?

SNPs NIH defines SNPs as: ‘Single nucleotide polymorphisms […] are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block, called a nucleotide. For example, a SNP may replace the nucleotide cytosine (C) with the nucleotide thymine (T) in a certain stretch of DNA. Do these variants influence significantly the phenotype? At which extent? Do genome-wide association studies provide a suitable tool to study informative or causal SNPs that can help us understand traits, conditions or genetic diseases?

GWAS With this approach, 300+ replicated associations were reported for more than 70 common diseases. Donnelly, P. 2008. Progress and challenges in genome-wide association studies in humans. Nature 456: 728-731.

GWAS Main limitations: Functional mechanisms are usually unknown. Detected SNPs are unlikely to be the functional variants; instead, they are proxies or sentinels. LINKAGE DISEQUILIBRIUM! Tantalizing results: associated loci often overlap between diseases (e.g. the same allele confers type 1 diabetes risk and protects against Crohn’s disease). Individual risk is assessed by genotyping the proxy SNP obtained from GWAS, which does not need to be the functional variant. This provides a noisy measure.

GWAS and hidden heritability GWAS generally capture a few percent of the estimated heritability for complex traits, so… What may explain the remaining heritability? Rare variants, other sources or variance, such as CNVs, epistasis, epigenetics, wrong estimates? Or just that complex traits are truly affected by thousands of variants of small effect (low-penetrance)?

GWAS and hidden heritability Gibson, G. 2010. Hints of hidden heritability in GWAS. Nature Genetics 2010. 42:558-560.

Hidden heritability example: height Height facts: Heritability ∼ 0.8 ≡ genetic factors account for about 80% of the variation. If we knew all the variants responsible for variation and calculated the additive effect, the difference between the top 5% and bottom 5% of the population would be about 26-29 cm (∼ size of an adult head). 2008: 3 GWAS identified 54 loci involved in height variation in the population. Critical features: Hundred of thousands of genetic markers genotyped Combined n = ∼ 63,000 people

Hidden heritability example: height 95 SNPs across all three studies 54 survived validation: 40 previously unknown variants. Three ‘overlapping’ genes: ZBTB38, HHIP, HMGA2. Average effect size: ∼0.4 cm (0.8 cm between the two homozygous). Lowering the significance threshold: Increase the power, but also more false positives. Main challenge: to overcome the trade-off. Effect sizes of individual variants are small  very large sample sizes are needed to detect associations. Future: prediction with genetic data alone. Visscher, PM 2008. Sizing up human height variation. Nature Genet. 40:489–490.

Hidden heritability: more examples Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls: proof of concept. 7 diseases 2000 cases/disease: proof of concept 24 independent association signals at P < 5x10-7: Bipolar disorder Coronary artery disease Crohn’s disease Rheumatoid arthritis Type 1 and type 3 diabetes Further 58 signals between 5 x 10-7 < P < 10-5 Proof of concept

Hidden heritability or phantom heritability? Explained heritability = Observed/Total estimated heritability = O/E. Classically: E is reliable, but O never reaches E, so we should keep looking for more variants to explain. Phantom heritability is created when E is overestimated. ‘The remaining […] is phantom heritability, which will never be explained by additional variants. It is the result of analyzing the data under an erroneous model’ (Zuk et al., 2012).

Hidden heritability: towards a systems biology approach