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            The analysis article is
based on the Genome-wide association studies of cancer, or

 

GWAS. GWAS is an approach to research the genetic basis of different diseases.

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example, with oncology, GWAS was performed and able to identify approximately
450 genetic

 

variants that are related to the risks, as well as provided proof of
polygenic susceptibility.

 

This review targeted on the functional basis and gene structure of
cancer susceptibility.  

 

            Regarding
cancer, there’s a pattern of relative risk or RR, that’s high in relatives of
patients who experienced early-onset cancer. Some risks in first-degree
relatives of patients accrued from twofold to threefold. Leukemia and testicular
cancers are exceptions where the RR were accrued from fourfold to eightfold. These
risks were thought to be more of a polygenic mechanism, which meant that it
involved multiple genes with small effects acting with non-genetic or
environmental factors. Three factors were observed in the relative risks. First
one was how not all of the patients were susceptible, second was how a fewer
number of the relatives were susceptible, and third was how the population
consisted of both susceptible and non-susceptible individuals. The familial
risks were compatible with the Mendelian predisposition, which happens when a
mutation in a single gene is competent enough to cause cancer in patients.

            Certain families of
CSG, or cancer susceptibility gene, with high penetrance of BRCA1

 

and BRCA2 were used for
identification by genetic linkage and biological research studies. Over

 

the past couple years, several efforts were placed into discovering what
high penetrance CSGs

 

for breast cancer has been made. However, no gene that was similar to
the profile of BRCA1,

 

BRCA2 or mismatched
repair genes have been identified. The data suggested that the missing

 

heritability would be polygenic. In Figure 1, the graph showed the low
RRs had low penetrance

 

genetic variants, moderate RRs had moderate penetrance genetic variants,
and high RRs had

 

high penetrance genetic variants. The higher the population frequency, the
more common of

 

where the low penetrance susceptibility genes would be.

 

            There
were two classes of cancer susceptibility variants identified. First one was the
rare-moderate penetrance variants that were known through the candidate genes,
which had a risk allele frequency of less than 2%. An example of this would be when
other genes encoded proteins in the DNA damage, which is associated with breast
cancer risk. The second one would be the common low-penetrance alleles, which
had risk allele frequency less than 5%. This reflected upon the various
subgroups of risk alleles that were detected. GWAS compared the frequency of common
DNA variations with healthy individuals. Figure 2 showed the performance of DNA
samples that were taken. The controls and patients’ DNA samples were genotyped
using genome platforms that analyzed genetic variations in single nucleotide
polymorphisms (SNPs). There should be a match between the patients and controls
to attenuate the false-positive associations. To minimize them, statistical
thresholds like Bonferroni correction is used.

            Bonferroni
correction is a common threshold for genome-wide significance of a P-value that
is less than 0.00000005, or a 5% significance level for millions of tests. The
threshold is used for sequencing studies or genome imputation of ten million
variants, assuming that linkage disequilibrium would be equivalent to a million
independent tests. Also, the imputation of genotypes by sequenced panels allowed
SNP alleles with frequencies lower than 0.1% to be imputed. This extended the
utility to figure out the allelic structure of the susceptibility.

            GWAS has been reported
for many major cancers in European populations, including

 

lung, pancreatic, gastric, breast, renal, and bladder cancers. Over 430
cancer associations at 262

 

genomic regions are known by this. Since that is the case, some
proportion of risk loci

 

that was related to the populations outside of Europe, would increase. The
distinction within the

 

heritability was also most likely to have influenced the risk loci.

About one-third of SNPs

 

mapped to genetic loci were connected to multiple cancers.

 

Pleiotropic loci would
be at the same location where the same association signal surrounded other
cancers. Pleiotropy enabled the grouping of loci or cancer by highlighting certain
mechanisms, such as how the SNP rs6983267 at 8q244.21 region was located in
between the scans of cancer and CRC or 19p13.11 region located near the breast
and ovarian cancer, as shown in Figure 3. Figure 3 displayed regulatory
interacts at the risk locus location. Part a showed relative location of GWAS
signals, part b showed epigenetic marks, or peaks, that represented histone
modifications, part c showed the sequence gene annotation, and part d showed
particular locus that was involved, such as chronic lymphocytic leukemia (CLL),
colorectal cancer (CRC), ovarian cancer (OvCa), etcetera.  

For other regions, the
genomic location was probably due to the molecular basis of associations that
were independent. Exploring the pleiotropic loci can lead to better insight of
cancer susceptibility, such as in cancer biology.

Few genes have been
implicated by GWAS and evaluated in association studies, such as microtubule
and chromosomal assembly or transcription regulation. There have been many insights
about how new pathways of cancer types emerged, such as loci that demonstrated a
cancer risk. An example would be the SNP at the 15q25 region since it was indirectly
linked to the lung cancer risk.

Many of the cancer
susceptibility loci are associated with the risks, except ones that were a
threefold increase risk of cancer. Some methods, such as genome-wide complex
trait analysis (GCTA), had shown that because of common variation, there was a
high proportion of heritable risks. However, there have been recent methods that
attempted to improve GCTA by fixing minor allele frequencies.

Referring back to
the SNP at 8pq24.21, most are likely to be in linkage disequilibrium with the
SNP. This region is also important how it is a good example of regulatory
mechanisms. To fix the risk loci, they used a method called fine mapping. Fine
mapping resolved association signals, as shown in Figure 4, where many classes
of genetic variation been identified to be the reasoning behind the risk loci.

Figure 4 displayed some
molecular mechanisms of risk SNPs. Because of the altering transcription factor
that bound through a looping promoter-enhancer interaction, the A>G
polymorphism affected gene transcription. Part a showed the A>G polymorphism
taking place at the intron splice site, which affected mRNA processing after.

Part b showed how the A>G polymorphism leads to microRNA binding side on
non-coding RNA, part c showed how the polymorphism affected the protein
sequence by an amino acid substitution of cytosine instead of tyrosine, and
part d displayed the SNPs. The coding variants could have more effects that did
not involve protein function.

Risk loci were used
to map out the genomic regions of chromatin and show the representation of the quantitative
trait in the loci. Chromatin was used to help link regulatory regions, which
was where SNPs localized to. A Hi-C analysis of the rs6983267 region demonstrated
a complicated mechanism, where large non-coding RNAs mediated effects at the
risk loci. Hi-C analysis is a chromosome conformation where crosslinked DNA
fragments are sequenced to infer with the structure of the genome. Also, susceptible
alleles increasing cancer risk can be enriched in the cancer relative to the
non-risk allele. Peripheral genes also affected function and regulation of core
genes, which can be shown in the figure.

Figure 5 displayed
the clinical aspect of GWAS. Through risk modeling, GWAS can identify the
individual affected by cancer and help prevent it for future generations. Cancer
biology can lead to Aetiology, which can lead to the screening of it or
prevention by therapeutics, or clinical application of GWAS, the clinical
application can lead to the risk modeling or therapeutics that can be prognosticated,
prevented, or drug repositioned. Cancer biology can also lead to
germline-somatic interactions.

Cancer genome
sequencing displayed evidence for the regulatory regions. However, more work has
to occur in order to identify which ones were the target genes and define the
germline-somatic interactions for the potential of GWAS. Polygenic risk score
(PRS) is used to optimize the efficiency of population-based screening programs
for detecting cancers. Usage of PRS is also used to provide information
regarding the cancer risk stratification in terms of Mendelian randomization
(MR) analysis. His analysis was used to identify the risks of nongenetic risk
factors. The challenge, however, is to make sure the genetic instruments used
were valid without including pleiotrophy and it might not be detected in an MR-based
analysis.

Overall, GWAS was
successful for identifying individuals who were at risk of different kinds of
cancers, and common genetic variations which affected drug efficacy. It also
demonstrated most of the common cancers is polygenic after all. The loci
identified have expanded the genes that took part in identifying the cancer
risk. Although some challenges still occur, advances in systems and strategies,
like CRISPR, is used to analyze further details. 

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