Review of paper by Morgan et al. 2018, on the use of GWAS with data of UK Biobank.

1. Genome-wide study of hair colour in UK
Biobank explains most of the SNP
heritability (Morgan et al. 2018)
Written by: Jacques Kadamani (8588619) and Yousef Layyous (300032210)

2. Outline
● Literature review research and hypothesis (Jacques)
● Study design, (Yousef)
● methods, and statistics (Jacques)
● Results (Jacques)
● Conclusion (Yousef)
● Study limitations and value of conclusion. (Yousef)

3. Literature Review
Natural hair colour is strikingly variable and is a complex genetics trait that is impacted by
relatively no non-genetic factors.
Hair colour is determined largely by three main cell types:
1. Melanocytes (Which produce melanin pigment)
2. Keratinocytes (Destination of the produced melanin)
3. Fibroblasts (Signals to and regulates the melanocytes)

4. Literature Review Continued
Several studies have examined the genetic basis of hair colour variation.
❖ Red hair is well associated with coding variation in the MC1R gene.
❖ MC1R is a G-protein-coupled receptor, which when activated, stimulated
alpha-melanocyte stimulating hormone, producing a dark eumelanin
❖ ASIP (Agouti signalling protein), if highly expressed, will produce a yellow sub-variation of
melanin, called pheomelanin.
Genome-wide association studies (GWAS) identified many genes that are associated with
different hair colour.

5. Hypothesis
Two main hypothesis are answered throughout this article:
1. What is the importance of the melanocyte-keratinocyte interactions in the determination
of hair colour pigmentation?
2. How does hair shape impact colour perception?

6. Study design
● Focused in variants of gene known to
be related to red and blonde hair color,
MC1R.
● Population: Genotypically confirmed
unrelated White British individuals.
● Related british and non-british used to
validade study.
● Penetrance was calculated

7. Study design
After determining possible SNPs, biological relevance was studied to increase validity of results,
or discard the association.

8. Study design
Epigenetic contributions were also evaluated, using rounds of random epistatic permutations.
Looking for overlap between histone modification and SNPs from GWAS.

9. Methods and Statistics
Study Participants. Individuals were derived from the UK Biobank that consisted of 502,655
participants, aged between 40 - 69 years. From these, 343,234 unrelated individuals from
British descent were analysed.
Genotype quality control. Variants included in the analysis were autosomal SNPs that had a X2
p-value of > 10-10
. The number of SNPs after quality control is 9,154,080.
Penetrance. The penetrance of allelic combinations was calculated by summing the number of
individuals of a particular hair colour with a given allelic combination over the total amount of
individuals with that allelic combination.

10. Methods and Statistics
Probabilistic inference of causal SNPs. Probability measures that calculates how likely a given
SNP is (SNP B), given that the prior SNP (SNP A) is the lead SNP. These were then plotted
against a normal distribution to verify results.
Epistasis. Verified the effects that certain genes had on other, non-allelic, genes. Allowed for the
correction on many epistasis signals located on Chromosome 16.
Hair colour scoring. To accurately calculate hair colour scores, a penalised logistic regression
model was created for blonde colour, including all lead variants.
Heritability. LD scores were used to calculate heritability for hair colour.

11. Results
❖ The strongest association with red hair is located around the MC1R gene, on
chromosome 16 (Graph A).
❖ 213 lead variants were associated with blonde hair (Graph B).
❖ 56 lead variants were associated with brown hair (Graph C).

12. Results
Epistasis was found between alleles at MC1R and other loci, namely with both rs1805005 and
rs1805008 and the HERC2/OCA2 regions.
❖ OCA2 variation affects the penetrance of the weaker red hair alleles of MC1R

13. Results
❖ The high penetrance allele of ASIP shows no
trans interaction (Graph B).
❖ The low penetrance allele of V92M and V60L
show interaction with ASIP, indicating
epistatic interaction (Graph C and D
respectively).

14. Results
❖ Chromatin enrichment for determining what each cell type is
responsible for concerning gene association and regulation.

15. Conclusion
● UK BioBanks has proven to be a powerful to discover new genetic association.
● 15 MC1R variants and some other loci explain 93% of heritability of red hair
● Almost a mendelian fashion
● New epistatic relationships may have been discovered.

16. Conclusion
● Blonde hair follows complex polygenic model;64 SNPs identified to explain 73%
heritability
● For brown hair 56 SNPs explain 47% heritability

17. Limitation and value of conclusion
● Results may only apply to white unrelated British
● Hair color was not objectively identified, but rather self-reported. Could invalidate
results.

18. References
1. Duffy, D., Box, N., Chen, W., Palmer, J., Montgomery, G., James, M., … Sturm, R. (2004). Interactive effects of MC1R and OCA2 on melanoma risk
phenotypes. Human Molecular Genetics, 13(4), 447–461. https://doi.org/10.1093/hmg/ddh043
2. Hysi, P. G., Valdes, A. M., Liu, F., Furlotte, N. A., Evans, D. M., Bataille, V., Visconti, A., Hemani, G., McMahon, G., Ring, S. M., Smith, G. D., Duffy, D. L., Zhu,
G., Gordon, S. D., Medland, S. E., Lin, B. D., Willemsen, G., Jan Hottenga, J., Vuckovic, D., Girotto, G., … Spector, T. D. (2018). Genome-wide association
meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability.Nature genetics,
50(5), 652–656. https://doi.org/10.1038/s41588-018-0100-5
3. Lin, B. D., Mbarek, H., Willemsen, G., Dolan, C. V., Fedko, I. O., Abdellaoui, A., de Geus, E. J., Boomsma, D. I., & Hottenga, J. J. (2015). Heritability and
Genome-Wide Association Studies for Hair Color in a Dutch Twin Family Based Sample. Genes, 6(3), 559–576. https://doi.org/10.3390/genes6030559
4. Marees, A. T., de Kluiver, H., Stringer, S., Vorspan, F., Curis, E., Marie-Claire, C., & Derks, E. M. (2018). A tutorial on conducting genome-wide association
studies: Quality control and statistical analysis. International journal of methods in psychiatric research, 27(2), e1608. https://doi.org/10.1002/mpr.1608
5. Shekar, S., Duffy, D., Frudakis, T., Montgomery, G., James, M., Sturm, R., & Martin, N. (2008). Spectrophotometric Methods for Quantifying
Pigmentation in Human Hair—Influence of MC1R Genotype and Environment. Photochemistry and Photobiology, 84(3), 719–726.
https://doi.org/10.1111/j.1751-1097.2007.00237.x