Cancer gwas and genetic risk prediction

COVENANT APPLIED INFORMATICS AND COMMUNICATION AFRICA CENTRE OF
EXCELLENCE (CApIC-ACE)
https://ace.covenantuniversity.edu.ng/
By: David Osarieme Enoma,
RA-CApiC-ACE
Supervisor: Prof. V. C. Osamor
Co-supervisor: Prof. O. O. Ogunlana

EXCELLENCE (CAPIC-ACE)
(Berndt et al., 2015)
CApIC-ACE Virtual Seminar Series 2020
2

© CApIC-ACE
 Prostate cancer (PrCA) is the second most
commonly diagnosed in the male population
globally.
 Determining which cancers are likely to progress
and cause death is of critical clinical importance,
especially in people of African Ancestry
 Gleason Score is a commonly used measure of
prostate cancer aggressiveness.
CApIC-ACE Virtual Seminar Series 2020 3

© CApIC-ACE
 Genome Wide Association Studies have identified 100
risk loci associated with prostate cancer risk however
none conclusively differentiates the aggressive from the
non-aggressive cases.
 Berndt et al., 2015 conducted a multistage GWAS for
prostate cancer among men of European ancestry using
the Illumina HumanOmni2.5 Beadchip.
 A total of 4,600 cases and 2,941 controls were genotyped
in Stage 1.
 In stage 2, a total of 6,575 cases and 6,392 controls from
five studies were genotyped with the custom iSelect

© CApIC-ACE
 Genotypes were analyzed using regression
models, assuming a log-additive genetic model
 Two previously published loci at chromosome 8q24
and 17q12 reached genome-wide significance in
stage 1.
 In a combined meta-analysis of the primary scan
together with the custom SNP microarray
replication and in silico look-up in a previous
GWAS, thirteen loci reached genome-wide
significance

© CApIC-ACE
 In the meta-analysis of the Gleason score results
for all three stages including a total of 12,518 cases,
three SNPs reached genome-wide significance.

© CApIC-ACE CApIC-ACE Virtual Seminar Series 2020 7
5q14.3 3q26.31
Pvaluesfromthelinearregressionmodels

© CApIC-ACE
 No SNPs were significantly
associated with the risk of the
aggressive phenotype when stratified
for the African American population
in AAPC.
 All data was imputed using
IMPUTE2 and 1000 Genomes Project
release version 3 as the reference
panel.
 Gleason score was modelled as a
quantitative trait as opposed to a
dichotomous outcome in order to
maximize the statistical power to
detect variants that differentiate
aggressive from non-aggressive
disease.
8

EXCELLENCE (CAPIC-ACE)
(Conti et al., 2017)
9

© CApIC-ACE
 Prostate cancer incidence is 1.6-fold higher in African
Americans than in other populations.
 African Ancestry Prostate Cancer GWAS Consortium,
Ghana Prostate Study (Cook et al., 2014) the Kaiser/
ProHealth Prostate Cancer Study and the
ELLIPSE/PRACTICAL OncoArray Consortium GWAS
results were combined.
 A total of 17.8 million genotyped and imputed single
nucleotide polymorphisms (SNPs).

© CApIC-ACE
 The risk alleles rs75823044 and rs78554043 are
found almost exclusively in African ancestry
populations.
 Genome-wide statistically significant associations
were also observed on chromosomes 13q34 and
22q12.1
 The most statistically significant PCa risk
association was observed with a novel triallelic
(A/T/G) variant at 8q24
 with the T allele found in approximately 12% of case
subjects and approximately 6% of control subjects

© CApIC-ACE
 10% of men with the highest
polygenic risk scores have a 3-fold
(95% CI = 2.52 to 3.63) of PCa
compared with men with “average”
risk
 A main limitation of this study is
suboptimal statistical power(<80%) to
detect modest effects (OR < 1.22)
 at genome-wide levels of statistical
significance for common alleles with
minor allele frequencies of less than
10% particularly in analyses of
disease aggressiveness.
14

(Huo et al., 2016)

© CApIC-ACE
 Breast cancer is a heterogeneous disease with
several molecular subtypes defined by biomarkers
such as oestrogen receptor (ER)
 Indigenous Africans have a higher proportion of ER-
negative breast cancer than African Americans
 The study consists of a discovery stage and a
validation stage.
 Huo et al. 2016 conducted a metanalysis on two
genome-wide association studies of breast cancer in
order to identify genetic risk factors.

© CApIC-ACE
 The discovery stage of the study was a meta-
analysis of GWAS in ROOT and AABC & replication
stage of the study was based AMBER .
 Genome-wide genotyping (Illumina Human Omni
2.5-8v1 array) and quality control, Imputation and
technical validation by sequencing (30x illumina
seq.) & Genotyping in replication stage (Illumina
Human Exome Beadchip v1.1) were performed.
 Odds ratios (OR) and 95% confidence intervals (CI)
were calculated from the multivariable logistic
regressions in statistical analyses.
 Principal components were computed using the
smartpca program in the EIGENSOFT package.

Figure 1. Manhattan plots of –log10 of P-values in the meta-analysis

© CApIC-ACE
 Huo et al. 2016 examined the
association of each SNP and breast
cancer risk using unconditional
logistic regression,
 adjusting for age, study site and the
first four eigenvectors from principal
component analysis.
 Huo et al., 2016 found that SNP
rs13074711, at 3q26.21, was
significantly associated
with risk of oestrogen receptor (ER)-
negative breast cancer
 [OR]=1.29, 95%; [CI]: 1.18 to 1.40;[P-
Value] =1.8x10e-8
 Only in African Ancestry
19

(Du et al., 2018)

© CApIC-ACE
 Studies of PCa among African men may inform the
contribution of genetic risk factors to the elevated
disease burden
 Association study of >100 previously reported PCa
risk alleles among 571 incidence cases and 485
controls among Uganda men.
 They characterized risk associations at known
PCa loci and constructed a polygenic risk model.

© CApIC-ACE
 Genotyping was done in UGPCS with the Illumina
OncoArray
 Phasing was done with SHAPEIT and imputed with
Minimac3 Version 1.0.12 done using the phase III
1KGP cosmopolitan reference panel
 PCA was performed using EIGENSTRAT together
with the 1KGP populations.
 For each SNP, per-allele odds ratios and P values
were estimated using unconditional logistic
regression, adjusting for age and the first 10 PCs
22
gim is the risk allele dosage for individual i at
SNP m; C defines a set of
92 non-8q24 risk SNPs with MAF >0.01 in
UGPCS ;

© CApIC-ACE
 In the genome-wide analysis, only five SNPs, all
located in the risk region at 8q24.21 were genome-
wide significant.
 Variant rs72725854 was found to be the
most statistically significant in association with PCa
risk (OR = 3.37, P = 2.14 × 10-11) .
23
A polygenic risk score for prostate cancer in Ugandan men

© CApIC-ACE
 The 8q24 risk region was also found
to be a major contributor to PCa risk
in Ugandan men.
 The African ancestry-specific risk
variant rs72725854 estimated to
account for 12%
of PCa in this population.
 They found that the known PCa risk
variants can effectively stratify PCa
risk in Ugandan men, with 10% of
men having a >4-fold increase in risk.
 A major limitation in this study is the
small sample size, with statistical
power for testing of known risk loci
and for risk allele discovery being
underpowered.
24

© CApIC-ACE
 Conti, D. V, Wang, K., Sheng, X., Bensen, J. T., Hazelett, D. J.,
Cook, M. B., Ingles, S. A., Kittles, R. A., Strom, S. S., Rybicki, B.
A., Nemesure, B., Isaacs, W. B., Stanford, J. L., Zheng, W.,
Sanderson, M., John, E. M., Park, J. Y., Xu, J., Stevens, V. L., …
Haiman, C. A. (2017). Two Novel Susceptibility Loci for Prostate
Cancer in Men of African Ancestry. JNCI: Journal of the
National Cancer Institute, 109(8).
https://doi.org/10.1093/jnci/djx084
 Cook, M. B., Wang, Z., Yeboah, E. D., Tettey, Y., Biritwum, R. B.,
Adjei, A. A., Tay, E., Truelove, A., Niwa, S., Chung, C. C.,
Chokkalingam, A. P., Chu, L. W., Yeager, M., Hutchinson, A., Yu,
K., Rand, K. A., Haiman, C. A., Hoover, R. N., Hsing, A. W., &
Chanock, S. J. (2014). A genome-wide association study of
prostate cancer in West African men. Human Genetics, 133(5),
509–521. https://doi.org/10.1007/s00439-013-1387-z
 Du, Z., Lubmawa, A., Gundell, S., Wan, P., Nalukenge, C.,
Muwanga, P., Lutalo, M., Nansereko, D., Ndaruhutse, O.,
Katuku, M., Nassanga, R., Asiimwe, F., Masaba, B., Kaggwa, S.,
Namuguzi, D., Kiddu, V., Mutema, G., Conti, D. V., Luke, A., …
Watya, S. (2018). Genetic risk of prostate cancer in Ugandan
men. The Prostate, 78(5), 370–376.
https://doi.org/10.1002/pros.23481
 Huo, D., Feng, Y., Haddad, S., Zheng, Y., Yao, S., Han, Y.-J.,
Ogundiran, T. O., Adebamowo, C., Ojengbede, O., Falusi, A. G.,
Zheng, W., Blot, W., Cai, Q., Signorello, L., John, E. M.,
Bernstein, L., Hu, J. J., Ziegler, R. G., Nyante, S., … Haiman,
C. A. (2016). Genome-wide association studies in women of
African ancestry identified 3q26.21 as a novel susceptibility
locus for oestrogen receptor negative breast cancer. Human
Molecular Genetics, 25(21), 4835–4846.
https://doi.org/10.1093/hmg/ddw305
25

Thanks For Your Attention !
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@CApIC ACE
@Capic Ace
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ace@covenantuniversity.edu.ng
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Cancer gwas and genetic risk prediction

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